Streptococcus mutans, Staphylococcus aureus, Staphylococcus epidermidis, Lactobacillus plantarum, and Candida albicans. The optical density of the formed biofilm was evaluated by the intensity of the experimental sample’s colour on a StatFax 303 Plus photometer at a wavelength of 630 nm. The combination of ascorbic acid and lysozyme hydrochloride in the established concentrations  resulted in a slight stimulation of cell proliferation without any toxic effects and increased antioxidant protection, preventing the development of oxidative stress. It was found that, in contrast to the separately used active substances, the combination of lysozyme hydrochloride and ascorbic acid inhibits the biofilm formation of all studied microorganisms and shows the ability to destroy diurnal biofilms of L. plantarum and fungi of the genus Candida, indicating potentiation and summation of the active pharmaceutical ingredients’ composition effects in the developed dental medicine. Due to the observed positive pharmacological and microbiological action, the combination of lysozyme hydrochloride and ascorbic acid in the medicated chewing gum serves as a promising tool for the prevention and treatment of infectious and inflammatory diseases of the periodontium and mucous membranes and the prevention of caries.Medicated chewing gum with lysozyme hydrochloride and ascorbic acid as active pharmaceutical ingredients was developed for application in dentistry. The aim of this research was to study the cytotoxicity, proliferative, and microbiological activities of the active ingredients in different types of cell cultures. The preclinical study of active pharmaceutical ingredients and their combinations was carried out using culture lines such as HepG2 (human hepatocarcinoma cells), Hek293 (human embryonic kidney cells), and MAEC . MTT assays were used to analyse cytotoxicity and proliferative activity, while the state of antioxidant protection was assessed by the content of sulfhydryl groups and catalase activity. The determination of lipid peroxidation products was based on the level of TBA-active products. As a microbiological model for studying the effect of the developed dental medicine on the ability of the oral cavity microorganisms to form biofilms, the following strains were used:  Compressed medicated chewing gum (MCG) is a promising and convenient dosage form of delivering drugs for the prevention and treatment of dental diseases ,2,3,4. WIn our previous investigations, we have substantiated the composition and the method of preparation of compressed medicated chewing gums, which were intended for the prevention and treatment of dental diseases, inflammatory diseases of the periodontium , mucous membranes (stomatitis), caries, and manifestations of xerostomia in particular . LysozymLysozyme is a glycoside hydrolase that catalyses hydrolysis of β1→4 bonds between N-acetylmuramic acid residues and N-acetyl-D-glucosamine in peptidoglycan, which is a major component of the Gram-positive bacterial cell wall. The hydrolysis disrupts the integrity of bacterial cell walls, causing bacterial lysis. Moreover, lysozyme can inactivate a layer of non-antigenic glycosaminoglycans in tumour cells and thus inhibit their growth ,7,8,9,10The important role of vitamin preparations in the complex therapy of dental diseases is well known. Ascorbic acid deficiency has been proven to cause dryness of the oral mucosa, gingival bleeding, and petechial haemorrhages in various parts of the mouth, resulting in xerostomia, ulcerative gingivitis, and stomatitis development ,15. In aTherefore, the combination of lysozyme hydrochloride and ascorbic acid in MCG composition will provide a complex effect on the tissues of the oral cavity: antimicrobial, anti-inflammatory, local immunomodulatory, antioxidant, and antiviral; it will enhance tissue regeneration and epithelialization; regulate blood clotting and normalize capillary permeability; stimulate salivation; and prevent plaque. In addition to this wide range of effects on the tissues of the masticatory and speech apparatus, ascorbic acid gives a pleasant taste to the developed gum.®, Bosnalijek, Sarajevo, Bosnia and Herzegovina; Lysopain® N, Sanofi-Aventis, Visp, Switzerland; Glossithiase®, Jolly-Jatel, Saint-Germain-en-Laye, France; etc.) varies from 5 to 20 mg, while the dose of ascorbic acid, according to the recommendations of the European Commission’s Council on nutrition labelling, is 80 mg/day [According to the literature, the amount of lysozyme hydrochloride in one dose of various oral preparations  of microorganisms that create dental plaque, which, according to recent studies, should be considered as a biofilm . The emeIn addition, the application of alternative methods of pharmacological screening of potential medicines for medicinal suitability is a key stage of drug development. Numerous achievements of fundamental and applied research in biology and medicine in recent decades are associated with the use of cultured cells of various genesis as sensitivity models.Streptococcus mutans (S. mutans), Staphylococcus aureus (S. aureus), Staphylococcus epidermidis (S. epidermidis), Lactobacillus plantarum (L. plantarum), and Candida albicans .Therefore, the aim of the present study was to determine the action effectiveness of the active pharmaceutical ingredients of the dental medicine in the form of compressed MCG, namely ascorbic acid and lysozyme hydrochloride, by microbiological and pharmacological evaluation using different culture of cells. For the study of proliferative, cytotoxic or metabolic effects, the embryonic cells, endothelial cells, and cell lines of hepatocytic origin were used. Since oral streptococci, opportunistic staphylococci, Candida fungi, and lactobacilli play a special role in the formation of dental plaque , strainsThe list of active pharmaceutical ingredients and excipients and their functions in the MCG formulation is presented in ® PWD 01 , and Syloid® FP244 , on which the Apple liquid flavour  was previously sprayed. At the last stage, the homogeneous mass of magnesium stearate  was added to this mixture. Round flat-faced chewing gums with a mass of 1000 mg and a diameter of 13 mm were compressed at a compression force of 7 kN on a laboratory single-punch tablet machine  [MCG’s preparation technology was as follows: powders of lysozyme hydrochloride , apple taste additive , and sucralose  were dry-blended, wetted with ethyl alcohol 96%, then granulated through a sieve and dried at room temperature. The obtained granules were thoroughly mixed with ascorbic acid , Health in GumUkraine) .The research was conducted in vitro according to the Ph. Eur. “Dissolution test for medicated chewing gums” (2.9.25) using a special apparatus that simulates the chewing process (type B) . DissoluBefore adding to the cells, aliquots containing the components under study were sterilized by filtering through 0.22-micron nitrocellulose filters.2 incubator at 95% humidity, 5% CO2 content, and a temperature of (37 ± 1) °C.The following cell lines were used in the experiment: HepG2—human hepatocarcinoma cells , Hek293—human embryonic kidney cells , and MAEC . Cultivation was performed in plastic Petri dishes and 96-well plate . DMEM medium  with 10% foetal calf serum (FCS)  or RPMI-1640 with HEPES, and 80 μg/mL gentamicin was used for cultivation. Cultivation was performed in a COThe action of active pharmaceutical ingredients was determined in comparison with the control (cells without tested compounds).2 incubator under conditions favourable for the reduction by cells of soluble MTT-reagent to insoluble formazan. After this time, the medium was removed from the wells, and 100 μL of dimethyl sulfoxide was added to solubilize formazan. The plate was gently shaken for 5–10 min until the formazan crystals dissolved [The determination of cytotoxicity/proliferative activity under the influence of test compounds was performed on the cells Hek293 using the MTT assay ,32,33. Aissolved ,35,36.Using a plate reader , the optical density of each well at 540 nm was determined, subtracting the measured background absorption at 620 nm. The results were calculated by the Formula (1) and given as a percentage of the values obtained for the control sample ,38:RAU =The calculation of the live and dead cell concentration ratio was carried out by routine counting in the Goryaev chamber after staining the dead cells with trypan blue.2O2 solution was used. To do this, part of the cells that reached 80% confluent was incubated for 4 h with hydrogen peroxide, after which ascorbic acid, lysozyme hydrochloride, and their combination, contained in aliquots obtained after MCGs dissolution, were added. The other half of the cells were incubated without the addition of hydrogen peroxide.The ascorbic acid and lysozyme hydrochloride effects on the performance of the antioxidant system have been determined in cells HepG2. To induce oxidative stress in cells, a 1 mM HThe concentration of TBA-active products was determined by the method of I. D. Stalna and T. G. Garishvili (1977) by reaction with 2-thiobarbituric acid (TBA) . The samThe content of TBA-active products per 1 mg of protein was calculated based on the value of the molar extinction coefficient of the complex of malonic dialdehyde with 2-thiobarbituric acid.2O2 was added to 25 μL of protein extract (0.025 mg of protein) and incubated for 10 min at room temperature. The reaction was stopped by adding 0.5 mL of a 4% ammonium molybdate ((NH4)2Mo7O24) solution. The mixture with 0.025 mL of distilled water instead of protein was incubated with the reaction mixture for 10 min at room temperature, and the reaction was stopped by adding 0.5 mL of a 4% (NH4)2Mo7O24 solution (zero point). The colour intensity was measured on a reader  at λ = 410 nm against the control sample (zero point).Catalase activity in cells was calculated by comparing the hydrogen peroxide content in the sample at the beginning of the reaction and after it ,41. For The level of total, protein-bound, and non-protein SH-groups was measured by the Elman method . The detThe determination of the non-protein SH-groups content was performed by adding 0.025 mL of 10.5% trichloroacetic acid to 0.075 mL of homogenate. The samples were incubated for 10 min at room temperature, followed by centrifugation for 15 min at 1500 rpm. The supernatant was neutralized by adding 1 M NaOH to pH = 7.0. For detection, 0.525 mL of 30 mM Tris-HCl with 1 mM EDTA (pH = 8) and 0.025 mL of Elman’s reagent were added and incubated for 30 min in the dark. Extinction was measured at λ = 412 nm against the control sample (0.625 mL of 30 mm Tris-HCl with 1 mM EDTA and the addition of 0.025 mL of Elman’s reagent).The determination of the protein SH-groups content was performed by subtracting the non-protein SH-groups content from the total content of SH-groups Formula (2):To determine the morphological characteristics of cells under the influence of active substances, mouse aortic endothelial cells of the MAEC line obtained from the collection of the company “Sigma”  were used as described by us earlier .To cells that were in 50–60% of the monolayer, 20 mg/mL of each of the studied compounds, contained in aliquots after MCGs dissolution, was added separately and in combination. After 24 h of cultivation, the culture medium was removed, the cells were washed twice with phosphate-buffered saline, and they were fixed with 95% ethanol for 30 min. Then ethanol was removed, and the cells were dried and stained with crystal violet dye for 20 min at a temperature of 37 °C. Assessment of morphological parameters and visualization of cell populations were performed using an inverted microscope AxioVert  equipped with AxioVision Rel. 4.8.2 software . Cellular preparations were photographed using a Digital Still Camera with a Carl Zeiss Vario-Sonar lens.S. mutans, S. aureus, S. epidermidis, and L. plantarum) and 25 °C . Washout from the agar culture was carried out by adding 1 mL of isotonic sodium chloride solution and bringing it to the turbidity standard, considering the amount of 109 cells/cm3.To study the ability of strains to form biofilms, their pure cultures were sown on nutrient agar and incubated in a thermostat for 24 h at 37 °C (Sterile flat-bottomed 96-well plates were used to detect biofilm formation. Each well was filled with 0.2 mL (200 μL) of the prepared suspension with the aliquots after MCGs dissolution that contained the studied substances individually and in combination (3 wells for each strain). As a control, only the appropriate nutrient medium is used to check for sterility and non-specific binding of the nutrient medium components to the tablet.Tablets were incubated for 4, 24, 48, and 72 h at 35 °C. After incubation, the contents of the wells were removed, and the wells were washed 4 times with 0.2 mL of phosphate-salt buffer (pH 7.2) to eliminate free-living “plankton” bacteria. The formed biofilms were fixed with a 2% solution (volume fraction) of sodium acetate and stained with a 0.1% solution of crystal violet for 30 min at room temperature. The excess dye was removed, the wells were washed three times with distilled water, and then the tablets were air-dried (30 min). To extract the dye, 0.2 mL of 96% ethanol was added to the wells and left for 60 min at room temperature ,45. The The obtained optical density values were taken as an index of bacterial adhesion to the surface and the ability to biofilms formation. The parameters given in the p < 0.05.Statistical processing of the obtained results was performed using the program “STATISTICA 8.0”. The probability of differences between the control and experimental groups was determined by the criteria of Student and Fisher. The level of reliability was taken at Alternative methods of determining the pharmacological action of potential medicines involve their cytotoxic/cytostatic screening on a panel of cultured cells with various sensitive markers for target exposure. In our experimental studies of the constituent components that saturate the MCG, the following cell lines were selected. Hek293 is a cell line of embryonic human kidney origin that mainly consists of endothelial, epithelial, and fibroblast cells. These cells are sensitive to cytotoxic effects and are therefore widely used in test systems in original studies of promising pharmacological drugs . When stThe aliquot samples obtained after the dissolution test with appropriate concentrations of active pharmaceutical ingredients were used in further studies of anti/proliferative effects.Determination of cytotoxicity/proliferative activity relative to cells of embryonic origin Hek293 under the influence of lysozyme hydrochloride and ascorbic acid was performed using an MTT assay. The MTT colorimetric assay is based on the ability of mitochondrial enzymes to convert the salt of 3--2,5-diphenyltetrazolium bromide  yellow colour into crystals of formazan purple colour, the intensity of which allows to determine the cytotoxicity/proliferation of cultured cells. Mitochondrial succinate dehydrogenase and cytochrome C are mainly involved in MTT reduction. Thus, compounds that affect the intensity of MTT reduction can act as mitochondrial respiration modifiers and, accordingly, can be used to determine viable cells.In order to study the effect of the studied active pharmaceutical ingredients on cells and to confirm the proposed concentrations, in this experiment, lysozyme hydrochloride and ascorbic acid were added to the composition of MCG in a wider range of concentrations—1–40 mg/per gum.As presented by the results of active pharmaceutical ingredients’ effects on human embryonic cells of the Hek293 line, lysozyme hydrochloride has not shown toxicity in the entire studied range of concentrations—the relative values of cell viability in comparison with the control were in the range of 79.87–92.43%; however, these data were not statistically significant A.When ascorbic acid concentrations of 1 to 20 mg/mL were used, no toxic effects were detected, but inhibition of proliferation was—the relative values of cell viability from the control were in the range of 79.3–106.8%. When ascorbic acid was added to the cells B, the opThe combined use of lysozyme hydrochloride and ascorbic acid in the range of studied concentrations also did not lead to a decrease in the number of viable cells C.However, this could not be said about the direct toxic effect of ascorbic acid on the studied cells—the inhibition of proliferation by higher concentrations can be explained by a shift of pH to the acidic side (≈4), because acidification of the medium did not allow normal cell regeneration . Thus, The results correlate with the experimental data of other scientists, who determined that the concentrations of ascorbic acid at 30 mg and 60 mg were too low to adversely affect the hard tissues of the teeth, with a positive effect on bleeding gums and a significant reduction in tartar and plaque after the use of chewing gum with ascorbic acid at these concentrations . In addiTherefore, testing the combination of lysozyme hydrochloride and ascorbic acid as components of MCG on cells of embryonic origin of the Hek293 line proved the effectiveness of these compounds: they did not show toxic antiproliferative effects but slightly stimulated cell proliferation, which would have a positive effect on the cells of the oral cavity in destructive-inflammatory diseases of the periodontium and mucous membranes.In the pathogenesis of periodontal tissues inflammatory diseases, a significant role is played by prooxidant-antioxidant homeostasis disorder, which is manifested by activation of lipid peroxidation and reduced enzymes activity of antioxidant protection ,57,58.2O2) are distinguished. Hydrogen peroxide is the least toxic and longest-lived (10−2 s) active oxygen metabolite and belongs to medium-strength oxidants. The generation of H2O2 occurs mainly on the nuclear, plasma, and mitochondrial membranes. Hydrogen peroxide easily penetrates cell membranes and is able to diffuse in the cell over considerable distances. There is an antioxidant defence system in the body to prevent the excess formation of reactive oxygen species, neutralize their damaging effects, and maintain cellular homeostasis [Among the main representatives of reactive oxygen species in a live cell, superoxide anion radical , causing damaging effects on membranes and cell structures. A simple and available method for determining lipid peroxidation products is the determination of TBA-active products .TBA-active products always have a constant content in tissues and cells, but with pathologies, their level can either increase or decrease. In the study of TBA-active products, it was found that their level under the action of lysozyme hydrochloride and ascorbic acid  did not differ from the control B, while So, it can be argued that ascorbic acid and lysozyme hydrochloride balance the antioxidant defence and inhibit lipid peroxidation.As the results showed, the degree of MAEC cells spreading on the surface of the Petri dish under the influence of lysozyme hydrochloride and ascorbic acid did not differ from the corresponding control. However, the addition of ascorbic acid separately and in combination with lysozyme hydrochloride changed the colour of the endothelial cells to bright pink, in contrast to the control, where the colour of the cells was purple, which is characteristic of this dye. Violet staining of MAEC cells was also observed when only lysozyme hydrochloride was administered. At the same time, no morphological changes were observed, and the number of dead cells did not increase. The change in the colour of the dye can be explained by a shift in the pH of the environment to the acidic side due to the penetration of ascorbic acid into the cells.Therefore, the conducted studies proved the absence of cytotoxic effects of active pharmaceutical ingredients on MAEC cells, which confirms the data obtained regarding the effect of the lysozyme hydrochloride and ascorbic acid combination on cells of other origin.A potential limitation of this manuscript is that it was only an in vitro study, as an in vivo test could provide a clearer understanding of the effects of the API in the medicated chewing gum composition on periodontal and mucosal tissues. In addition, specific types of cell lines were used in the study, such as HepG2, Hek293, and MAEC. In further studies, it is planned to conduct additional experiments using periodontium and mucous membrane-derived cells to conclude about the positive role of the combination of lysozyme hydrochloride and ascorbic acid in the prevention and treatment of destructive and inflammatory periodontal diseases and mucous membranes. In this pre-screening study on the combined effect of the medicated chewing gum components in the therapeutic range on cell cultures, no cytotoxic/cytostatic effect of these components was recorded. It is assumed that in the prevention and treatment of destructive and inflammatory diseases of the periodontium and mucous membranes, these components will not cause side effects. However, to confirm the therapeutic effectiveness of these components, we plan to determine the anti-inflammatory effect, namely the expression of anti-inflammatory cytokines by lymphoid cells under the influence of ascorbic acid and lysozyme hydrochloride, in further studies.The study of the APIs effect on biofilm formation was carried out in 2 stages.The purpose of the first stage was to determine the influence of both the individual components of the chewing gum sample and their combined use on the process of biofilm formation. The results are presented in L. plantarum biofilms by 6.4 times compared to the optical density of biofilm before the application of these API combinations , and S. mutans by 5.6 times compared to the control . Regarding representatives of staphylococcal microorganisms, the ability of the combined use of APIs to prevent biofilm formation was also determined: the optical density of diurnal S. aureus biofilms decreased by 4.7 times and that of S. epidermidis by 4.4 times compared to the control. The tendency to inhibit the formation of biofilms was also established for C. albicans—the optical density decreased by 3.5 times regarding the control.The results in The next stage of our research was the study of the active components ability to destroy biofilms of microorganisms. The results are given in L. plantarum and C. albicans, which exceeded the control results by 8.3 and 4.2 times, respectively. With regard to the diurnal biofilms of other microorganisms, it was established that the studied substances, both individually and in combination, do not reveal a statistically significant difference compared to the control.As shown in the results presented in In these pharmacological and microbiological research using various cultures of cells, the safety issue and effectiveness of the combined use of lysozyme hydrochloride and ascorbic acid  in compressed medicated chewing gum for prevention and treatment of dental diseases were investigated for the first time.The individual and combination impact of active pharmaceutical ingredients on human embryonic kidney cells of the Hek293 line revealed no toxic antiproliferative effects, which may indicate a positive pharmacological action of the lysozyme hydrochloride and ascorbic acid combination in the studied therapeutic range on the cells of the oral cavity in destructive-inflammatory diseases of the periodontium and mucous membranes.In the study of prooxidant-antioxidant homeostasis, which is manifested by the activation of lipid peroxidation processes and a decrease in the activity of antioxidant enzymes on cell cultures, it was confirmed that these active ingredients in the established concentrations promoted antioxidant protection and prevented the development of oxidative stress—one of the leading links in the development of inflammatory diseases of periodontal tissues. The results also showed that monocomponent therapy with each of the active substances did not have a significant effect on the studied parameter. On the contrary, their combined use increased the antioxidant action, which indicates potentiation and summation of the effects of lysozyme hydrochloride and ascorbic acid.The morphology of the cells is not affected by lysozyme hydrochloride, ascorbic acid, or their combination, which confirms the safe choice of API concentrations and indicates the absence of side effects of dental chewing gums on cell viability.L. plantarum, S. mutans, S. aureus, S. epidermidis, and fungi of the genus Candida), which also indicates the potentiation and summation of the active ingredients’ effects of the developed dental medicine. The combination of lysozyme hydrochloride and ascorbic acid also demonstrated a reliable ability to destroy diurnal biofilms of the bacteria L. plantarum and fungi of the genus Candida.Based on the microbiological studies, it was established that under the influence of the API combination of the medicated chewing gum, in contrast to their separate use, there is a tendency to suppress the biofilm formation of all studied microorganisms (bacteria The obtained results, reflecting the biological behaviour of the tested active substances in combination and their positive influence on molecular mechanisms in living cells, will be considered in further pharmacological studies of the developed compressed medicated chewing gum.


With the progress of science and technology, artificial intelligence is widely used in various disciplines and has produced amazing results. The research of the target detection algorithm has significantly improved the performance and role of unmanned aerial vehicles (UAVs), and plays an irreplaceable role in preventing forest fires, evacuating crowded people, surveying and rescuing explorers. At this stage, the target detection algorithm deployed in UAVs has been applied to production and life, but making the detection accuracy higher and better adaptability is still the motivation for researchers to continue to study. In aerial images, due to the high shooting height, small size, low resolution and few features, it is difficult to be detected by conventional target detection algorithms. In this paper, the UN-YOLOv5s algorithm can solve the difficult problem of small target detection excellently. The more accurate small target detection (MASD) mechanism is used to greatly improve the detection accuracy of small and medium targets, The multi-scale feature fusion (MCF) path is combined to fuse the semantic information and location information of the image to improve the expression ability of the novel model. The new convolution SimAM residual (CSR) module is introduced to make the network more stable and focused. On the VisDrone dataset, the mean average precision (mAP) of UAV necessity you only look once v5s(UN-YOLOv5s) is 8.4% higher than that of the original algorithm. Compared with the same version, YOLOv5l, the mAP is increased by 2.2%, and the Giga Floating-point Operations Per Second (GFLOPs) is reduced by 65.3%. Compared with the same series of YOLOv3, the mAP is increased by 1.8%, and GFLOPs is reduced by 75.8%. Compared with the same series of YOLOv8s, the detection accuracy of the mAP is improved by 1.1%. With the rapid development of UAVs , the appBenefiting from the common progress of algorithms, data and computing power, artificial intelligence  technoloCompared with the other algorithms, YOLOv5 belongs to the one-stage object detection method, which plays an excellent role in various fields of society. The main idea is to divide the whole image into several grids. Each grid predicts the type and location information of the objects in that grid. Then, the target box is filtered according to the IoU  value be(1)In order to solve the problem of gradient disappearance or explosion and equal distribution of attention weights caused by the deepening of the model network, a CSR module is set to make the stable transmission of feature information and always pay attention to effective information.(2)Aiming at the problem of missing location information caused by continuous convolution, feature fusion is used to fuse deep semantic information and shallow location information to improve the generalization ability of the model.(3)Increase the size of the detection head by up-sampling twice to obtain a smaller feature prediction map, improving prediction probability. At the same time, a more suitable anchor box is selected to improve the detection accuracy.Based on the consideration of UAV deployment application and the generalization detection of small target objects, this paper proposed a novel improved algorithm based on YOLOv5s named UN-YOLOv5s. The main contributions of the proposed algorithm are as follows:YOLO ,11,12 isDepending on the difference between depth multiple (dm) and width multiple (wm), YOLOv5 comes in four versions, YOLOv5s  is the minimal network model, YOLOv5m  is the medium network model, YOLOv5l  is the large network model, and YOLOv5x  is the largest network model.The loss  calculatClasses loss: BCE loss is adopted and only the classification loss of positive samples is calculated.Objectness loss: BCE loss is adopted, and obj loss is calculated for all samples, where obj refers to the CIoU of the target bounding box predicted by the network versus the GT Box.Location loss: CIoU loss is adopted and only the location loss of positive samples is calculated.LOSS is the total loss function of the model, The network structure of YOLOv5 is mainly composed of Backbone, Neck and Head parts. Backbone used Focus, Cross Stage Paritial Darknet53 (CSPDarknet53) and Spatial Pyramid Pooling–Fast (SPPF) modules. The CSPDarknet53 module improved the feature extraction ability of the model to a certain extent by using the residual structure and feature reuse mechanism and it prevented the gradient from disappearing and exploding. The SPPF module solved the multi-scale problem of the target well by fusing different sizes of MaxPool. The Neck part used Feature Pyramid Network (FPN)  + Path AThe deepening of the number of network layers will bring the improvement of accuracy. At the same time, the phenomenon of gradient disappearance or a gradient explosion can also occur. The concept of a residual network was introduced in this module, adding the standard residual structure . ThroughThe input image feature information is transmitted in two ways: a and b. Path a is a standard residual structure, and because of the existence of this branch, the loss can directly transmit the gradient to the previous network through this shortcut when the network back-propagates, which effectively alleviates the gradient disappearance and gradient explosion. Path b first goes through a convolution module, and then it is divided into two paths, c and d; c passes through a convolution module, d passes through the BottleNeck1 module and a convolution module. After, the two paths c and d are Concat into path b. Then, the features of a and b are connected. The total feature information undergoes a convolution module and then receives the weight distribution of SimAM attention, so as to pay attention to the links that need more attention.CSR modules (C stands for convolution module, which mainly performs feature extraction. S stands for SimAM  attentioBased on the definition of absolute scale and the actual application of UAV shooting scenes, the YOLOv5s algorithm finds it difficult to capture small targets with low resolution due to its large down-sampling multiple, deep feature layer, small detection head size, and large anchor box . The detIn the UN-YOLOv5s algorithm, the MASD mechanism removed the concept of large target detection and focuses on the detection of small and medium targets in UAV shooting. The 20 × 20 detection heads were removed in the proposed method, The size of the detection head approached 160 × 160 by combining up-sampling and the C3 module, increasing the scale of the detection head while extracting target features stably. In this way, the feature map grid will get a more detailed distinction, and the feature extraction for small targets will be more targeted. Therefore, the detection effect of small target objects will be significantly enhanced. The final sizes of UN-YOLOv5s detection heads are 40 × 40, 80 × 80, and 160 × 160. Not only that, we changed the anchor size in the original algorithm. It is more effective to take small targets, successfully removing most of the redundant information, and is more suitable for small target detection in aerial photography. In the process of a convolutional neural network , the deeThe MCF path of the UN-YOLOv5s algorithm fused the information of different layers and extracted the deep semantic information while introducing the shallow location information. The algorithm used FPN  to up-saBased on the above three innovations, the construction of UN-YOLOv5s is completed. It effectively improved the detection performance of the original algorithm for aerial images. Using the CSR module, implementing gradient optimization to ensure the stable transmission of information, the weight distribution of important factors is completed. Using the MASD mechanism, the accurate positioning of small target detection is realized and redundant information is removed to ensure the effectiveness of the information. Using the MCF path, by fusing shallow location information with deep semantic information, the richness of the feature information is improved. The UN-YOLOv5s and YOLOv5s algorithms were compared on the VisDrone official dataset  and the The experiment was based on a pytorch framework  and progThe UN-YOLOv5s algorithm can effectively detect targets in remote places in the picture, monitored people in dense areas, reflected traffic data on crowded roads, reflected driving safety data at night, and reflected regional play data. Not only that, the detection capability of more scenes has also been greatly upgraded, which will not be introduced in detail here. Deployed on UAVs, it is convenient for managers to take safety measures and deal with accidents, which is of great engineering significance.This part describes the improvement effect of each improvement module on the basis of YOLOv5s. The mAP@0.5 accuracy of UN-YOLOv5s is 8.4% higher than that of YOLOv5s. It is 2.2% higher than YOLOv5l. It is 1.8% better than YOLOv3, It is 1.1% higher than YOLOv8s. It is worth mentioning that the GFLOPs of YOLOv3 algorithm is 154.7. YOLOv5l has a GFLOPs of 107.8. However, the GFLOPs of UN-YOLOv5s is only 37.4. Compared with the YOLOv3 algorithm, it is reduced by 75.8%. Compared with YOLOv5l, it is reduced by 65.3%. While the accuracy improved, the running speed is also guaranteed. Regarding the UN-YOLOv5s algorithm, the performance is much better than YOLOv3, YOLOv5s, YOLOv5l, and even better than YOLOv8s.A UAV-based detection algorithm, while ensuring the improvement of detection accuracy, is necessary to ensure the real-time performance of detection, validity, and ease of deployment. This algorithm has high detection accuracy. The theoretical basis and practical effect are relatively mature, easy to deploy, and have good versatility. On this basis, by constantly understanding the data of aerial images, it is evident that small and medium targets account for the vast majority of aerial images. The detection head and anchor box adapt the UAV to capture medium and small targets. By fused paths of more scales, the target feature information and location information are mined, and the target features are more accurately locked. The CSR module is added to stabilize information dissemination and focus on important information factors. Finally, the mAP of the proposed algorithm is improved by 8.4% compared with the original algorithm in the VisDrone dataset . We are


The purpose of this work was to develop a substitute material model for the analysis of reinforced concrete structures. This paper presents proposals to solve the problem of limited calculation time, both to perform simulation models and to perform effective numerical or analytical analyses of structural elements in order to achieve results consistent with experimental results. Achieving this aim is conditional upon the determination of the material model parameters, taking into account the type of structure, the system of reinforcement, and the static strength–deformation parameters of the component materials. A universal procedure is proposed for determining the parameters of the substitute material model on the basis of the homogenization function, in which the homogenization coefficient is assumed as being equal to the effective reinforcement ratio of real reinforced concrete structural elements. In addition, the introduction of a new concrete constraint coefficient to this procedure, which corresponds to the proportionality coefficient of biaxial to uniaxial compressive strength, is proposed. On the basis of the conducted comparative analyses, the possibility of using the hypothetical substitute material model for the design of building elements and structures was confirmed. The average values of the obtained results for individual research series did not differ from the experimental results by more than 8.5%, for both the numerical and analytical models. The behavior of the numerical model of a structural system depends strictly on the material model used, which is an integral part of the procedure for modeling the structure of a building. Performing a spatial analysis of the behavior of a structural element or the entire structure of a building requires a complex numerical model. The difficulty of spatial modeling of reinforced concrete elements using high-strength concrete (HSC) and ultra-high-performance concrete (UHPC) lies in the separate modeling of structural materials (concrete and reinforcing steel) with their contact conditions. With a large number of reinforcing bars, each of them must be defined by its interaction with concrete, which increases the number of nodes used in the computational analysis and the analysis time. Due to the difficulties in modeling the properties of complex structural systems with heterogeneous reinforced concrete structures, an approach based on the homogenization of reinforced concrete is used. In one study , the resIn another study , an apprModeling the behavior of structural elements with increased mechanical and strength parameters ,4,5,6 reFurthermore, in , the autIn another study , solutioIn , the autThe authors of  presenteThe authors of  presentew/c) to the water–cement ratio, the silica–cement ratio, cement class, and aggregate characteristics [This study proposes a methodology based on the expected compressive strength of HSC and UHPC concretes. The basic formula for designing the compressive strength of concrete is Feret’s law, which relates the compressive strength of high-strength concrete (HSC)  to the substitute material model, depending on the adopted type of reinforcing steel  [In the substitute material model, it is also possible to take into account the strain rate effect by introducing the generalized dynamic strength coefficient mula (4) :(4)fhcd=cG and tensile deformation ctG are indicated in Gcε and Gctε correspond to the surface area of the energy cG and ctG, and are taken as limit strains of concrete failure in compression and tension. In this way, we can determine the area of potential failure of finite elements in the model as the point of failure in the model. The schematic diagram of a substitute model is presented in The following homogenized model parameters are specified:hP = .The following strain limits describe the homogenized model:hε = ; and t,limε—the strain limit corresponding to the minimum tensile strength (at rupture).The reference model of concrete was described in detail in ,22,23.In In the developed procedure, the compressive strength of concrete was determined on the basis of Formula (5) ,17 usingLtf—the compressive strength of the concrete at the time of t days (MPa); cemf—the strength of the cement as measured on ISO mortar or design value (MPa); kk = 3.0 + (Bρ − sB)/Bρ—the aggregate coefficient, where sB is the number of binders and aggregates lower than 0.2 mm in size per kg in 1 m3 of concrete, and Bρ is the bulk density of all the aggregates in the sample (kg/m3); frk = exp(0.034Sρ)—the reinforcing-fiber coefficient, where Sρ is the percentage ratio of steel-fiber mass to the mass of the cement (%), hereinafter frk = 1.0 for Sρ = 0.0 is taken; szk—the specimen shape and size coefficient (according to [W/(C + 0.22sm)—the total amount of water included in all the mixture elements (including the water in the SP) relative to the amount of cement with 0.22 microsilica fume amount; and sm/C—the ratio of the microsilica fume to cement.rding to ); kt=1−eThe procedure was carried out in such a way as to avoid the need to carry out experimental tests on standardized samples. The flexural tensile strength of concrete was determined on the basis of Formula (6) and In the next step, the value of concrete tensile strength was determined according to Formula (7) in which the influence of the beam height was taken into account :(7)fct=0cE is determined on the basis of Formula (8) [The elastic modulus mula (8) :(8)Ec=22cε at reaching the maximum compressive strength is determined on the basis of Formula (9):The strain cuε for compressive strength is determined on the basis of Formula (10):The ultimate strain ctε) and ultimate (ctuε) tensile strains were determined according to Formulas (11) and (12), respectively.The maximum . The schematic diagram of the substitute material model for analytical calculations is shown in For the application and verification of the substitute material model in analytical calculations, the moment–curvature relationship is presented. The moment–curvature relationship for the cross-section made of substitute material was determined on the basis of the analysis of work phases of a uniform bending cross-section, assuming an elastic–ideally plastic stress distribution with different compressive and tensile strengths of the material, and taking into account the unlimited deformability of the material for tensile strength, as shown in In phase Ia, the resistance of the cross-section is determined by the elastic stresses in the compression zone and the tension zone of the cross-section. In phase Ib, the stresses in the compression zone remain elastic, and the stresses in the tension zone reach the tensile strength of the replacement material. In phase IIa, the stresses in the compressed zone remain elastic, and the strains in the tension zone exceed the value of the tensile strain limit of the replacement material, and then stresses appear in the lower cross-sectional zone equal to the minimum strength of the replacement material. In phase IIb, the compressive stresses reach the compressive strength of the replacement material. In phase IIIa, deformations in the compressed zone exceed the limit deformations of the substitute material for compression, and a zone with a minimum strength of the substitute material is formed. In phase IIIb, the theoretical limit load capacity of the cross-section is reached, which is determined by the rigid–plastic distribution of stresses equal to the minimum strength of the substitute material. In Verifications of the substitute material model in numerical calculations for ordinary concretes are presented in ,8, whileBased on the test results presented in this paper, the parameters of the substitute material were determined in two variants. In variant 1, all parameters of the substitute material were determined on the basis of static and strength parameters being the results of the authors’ research. In variant 2, the parameters of the substitute material were determined based only on information on the materials, i.e., the cement used, the concrete recipe, and the steel grade. Other parameters were determined on the basis of dependencies indicated in this study, results for series B20-B30 are shown in We noticed that the compressive strength of concrete, according to the authors of experimental research  and our Based on the determined parameters of the substitute material model, a numerical analysis was carried out using the Abaqus 6.23 software. Verifications of the numerical substitute model were made on the basis of a comparative analysis of the authors’ own results and the experimental results of Feng et al. . In FiguIn turn, The difference in the displacement point of 47.0 mm was 2.85 kNm for the B20R3-SB series, which was about 1.9% of the breaking force, while for the B20R3-B series, it was 11.1 kNm and 7.5%, respectively. The difference in the displacement point of 52.0 mm was 7.47 kNm for the B30R3-SB series, which was about 4.7% of the breaking force, while for the B30R3-B series it was 12.85 kNm and 8.2%, respectively. It should be emphasized, however, that according to the EC2 standard, the maximum permissible degree of reinforcement of structural elements is 4%. The R3 series analyzed in the article were marked as having 2.9% of reinforcement, while taking into account the upper reinforcement of 3.5%, which is close to the limit value. In most traditionally designed structural members, the economic reinforcement ratio is between 0.8% and 2.0%. In this respect, the convergence of the results according to the described procedure is the highest.(1)Modeling the substitute material model according to the proposed procedure allows for the achievement of numerical results convergent with experimental results for reinforced elements made of UHPC concrete. The average results of displacements in the middle of the beam for the series using the experimental results of the concrete itself differ from the authors’ experimental results by 5.4%, while for the series in which all strength and deformation parameters were determined the results differ by 8.5%.(2)The difference between the results using the analytical substitute model with the experimental results is in the range of 1.9% to 8.2%.(3)In the case of structural elements with a large amount of longitudinal reinforcement, it is recommended to take into account the work of the substitute material in terms of biaxial compression by applying an additional multiplier called the concrete constraint coefficient with a value in accordance with the values in the range (1.16–1.2) adopted in the literature.(4)The highest accuracy of displacement results equal to 1.9% was achieved for the B20R1 series using the results of experimental studies.(5)The highest accuracy of the breaking load results equal to 0.5% was achieved for the B30R1 series using the results of experimental tests.(6)The average displacement results for the R1 and R1-S series reached an accuracy of 5.1%, while for the R3 and R3-S series, it was 11.0%.Based on the tests and analyses carried out, it was found that the procedure for designing a substitute material model allows for achieving satisfactory results in the modeling of structural elements. Results similar to experimental results, and similar approximation to analyses using other analytical or numerical models, confirm the possibility of using homogenization for structural elements made of HSC and UHPC concretes. The following conclusions emerge from the conducted analyses:The presented results of this study confirm the high efficiency of the proposed method for the homogenization of reinforced concrete structures.The modeling methodology based on the homogenization theory can also be applied to structural elements reinforced with the use of nonmetallic reinforcement because it consists of modifying the basic deformation and strength parameters of the concrete model used. The method can also be used to modify other material models for hybrid and composite elements.The model can be used for any configuration of longitudinal and transversal reinforcement, but we must remember that structural elements and the entire building must be designed in accordance with applicable standards, which specify, among others, the minimum amount and shape of individual types of reinforcement.


The frame of corn harvester is prone to vibration bending and torsional deformation due to the vibration caused by field road bumps and fluctuations. It poses a serious challenge to the reliability of machinery. Therefore it is critical to explore the vibration mechanism, and to identify the vibration states under different working conditions. To address the above problem, a vibration state identification method is proposed in this paper. An improved empirical mode decomposition (EMD) algorithm was used to decrease noise for signals of high noise and non-stationary vibration in the field. The support vector machine (SVM) model was used for identification of frame vibration states under different working conditions. The results showed that: (1) an improved EMD algorithm could effectively reduce noise interference and restore the effective information of the original signal. (2) based on improved EMD – SVM method identify the vibration states of the frame with the accuracy of 99.21%. (3) The corn ears in grain tank were not sensitive to low order vibration, but had an absorption effect on high order vibration. The proposed method has the potential to be applied for accurately identifying vibration state and improving frame safety. It often brings many serious hazards in engineering, and is usually the direct cause of mechanical and structural damage and failure. For example, due to vibration, the machining accuracy of machine tools is reduced, maintenance costs are increased, electronic equipment cannot work properly, and vehicle frames fail and fracture . In addiFor vibration problems, a great deal of research has been done by domestic and foreign scholars. Frequency response function (FRF) is a common method and it represents the relationship between input and output when excitation and response signal are known . ParticuFor the case that only the output signal is known, the output-only modal identification method is widely accepted in the field of vibration. This method could directly identify modal parameters from the structural response signals . Blind sIn addition to the above methods, the identification of structural modal parameters by finite element software is also a universal method in vibration research. Modal parameters are the function of structural physical characteristics. Its modal parameters  will also change when the structure is damaged or the physical parameters  change. Bum et al. used first order analysis technology to design a vehicle sub-frame and proposed an equivalent model of vehicle sub-frame composed only of beam elements . ShrinidHence, this paper starts from the perspective of signals to study frame vibration states of corn harvester and explore the vibration mechanism. The absorption of vibration energy by corn ear is different for every transportation condition when the grain tank is full. On the micro level, it is reflected that corn ear may hinder a certain order or several orders of the signal but is not sensitive to other orders, making the original signal become the superposition of the remaining order signals. On the macro level, the original excitation signal is distorted, and it is different from the theoretical calculation value. It will lead to abnormal vibration of the frame and even resonance, which seriously affects the reliability of the machine. The vibration signal will show the characteristics of non-stationary and high noise caused by its many parts, complex structure, and the influence of field road fluctuation and turbulence for the corn harvester. The EMD has the advantages of adaptive decomposition of noisy and non-stationary signals without considering the basis function . It can Deep learning is a special type of machine learning methods capable of extracting the optimal input representation directly from the raw data without user intervention . It is pThe main work of this research as follows:(1) For the non-stationary and high noise characteristics of field vibration signal, the improved EMD method could effectively reduce noise interference and restore the effective information of the original signal.(2) The SVM model based on power spectrum entropy (PSE) and standard deviation (SD) is established. Optimizing model parameters, and results are compared and analyzed.(3) The established model realizes the identification of frame vibration states for corn harvester under different working conditions.22.1In this study, Yitong Manchu Autonomous County, Siping City, Jilin Province, located in northeast China, was selected as test area. The harvesting machine used was the self-propelled 4YZP-4Y harvester jointly developed by Jilin University and Shandong Juming Machinery Co., LTD., as shown in 2.2In this section, we first review the standard EMD algorithm, and then introduce the calculation process of the proposed algorithm in detail.2.2.1The EMD performed signal decomposition according to the time scale characteristics of the data itself without setting any basis function in advance . It coul(1) The signal has at least two extreme points, a maximum and a minimum;(2) The characteristic time scale is defined by the length of time between two extreme points;(3) If the signal data lacks extremum points but there are deformation points, the extremum points can be obtained by differentiating the data once or several times, and then the decomposition results can be attained by integration.The specific decomposition process of the EMD can be divided into the following steps, as shown in 2.2.2(*)The main limitation of the EMD was mode mixing . The rooStep1. Applying equations (1) to (7) to obtain the Hilbert marginal spectrum of the IMF1. Specifically, it can be described as:x(t) is decomposed by standard EMD to obtain IMF1. Hilbert transformation is performed on the IMF1 to obtain v(t), namely:(1) The original signal (2) Construct the analytic signal:(*) If you need the source codes for this article, please contact the author at zrq@jlu.edu.cn(3) The amplitude and phase functions are obtained:(4) The instantaneous frequency is further calculated as follows:H:(5) Continue to obtain Hilbert spectrum (6) Hilbert marginal spectrum is obtained:In equation (6), RP represents the real part, T represents the total length of the signal in equation (7).y(t) is constructed by equation (8) to (9). It can be described as:Step2. The analytic signal (1)The average instantaneous frequency of the IMF1 can be calculated by equation (8) according to the energy mean method .1(t)a is the Hilbert envelope amplitude of the IMF1, 1(t)f is the instantaneous frequency of the IMF1.Where y(t):(2)Solve the analytic signal sf is the sampling rate of the signal; As a rule of thumb, 0a is 1.6 times the average amplitude of the signal component.Where y(t) is mixed with the original signal x(t) to obtain 1(t)x and 2(t)x:Step3. The analytic signal 1(t)x and 2(t)x respectively, and the intrinsic modal functions 1(t)L and 2(t)L are attained. The intrinsic modal function of x(t) is obtained as L(t).Step4. the EMD algorithm is used to decompose 2.3In statistics, the SD reflects the dispersion of a data set. In the time domain vibration signal, it could reflect the change of signal energy . The valThe SD is the root of the sum of the squares of the deviations. The exact value can be calculated by the equation (13):n is the length of the entire data x.where The PSE is the extension of Shannon entropy in the frequency domain, which is related to the distribution of frequency components. The calculation method of the PSE is as follows :x(t) can be attained by equation (14).Step1. The power spectrum of signal N is the length of the data x(t). X(w) denotes the Fourier transform of x(t) by the fast Fourier transform (FFT).where Step2. According to equation (15), the probability density function of the spectrum is estimated by normalizing all frequency components:i)s. Then, the relative energy ratio of each IMF was calculated, and several IMFs with high relative energy ratio were selected to combine into a new signal. In order to reduce the amount of calculation, the PSE and the SD were calculated for every 200 data, and 326*2 feature sets were obtained; Working conditions S1, S2, and S3 were labeled as Label1, Label2, and Label3 respectively; Secondly, a random function was used to scramble the ordinal matrix to get a number sequence again to avoid overfitting; Next, 1 to 250 rows were used as the training sets and 251 to the last rows were used as the test sets; Finally, the SVM was used to establish frequency recognition model.For the SVM model, if the parameters c and γ are too small, it may be underlearned, and vice versa, it will be overlearned. All of these situations will affect the accuracy of model training. Hence, finding appropriate parameters is particularly critical to the learning ability of the model. In this study, we used particle swarm optimization (PSO), genetic algorithm and grid search to optimize both two parameters respectively. The whole algorithm is summarized in Algorithm 1 Proposed algorithm.Input:Original signal matrix AA, sampling frequency sf, empty column vector y, threshold ϵ.1:IMF←emd(AA)2:Find the number of columns of IMF and label it n3:for 1 to n4:x←IMF(i),:5:Solve (3) to obtain amplitude mA6:A←1.6*mean(Am)7:Obtain i(t)L by (9) to (12)8:Calculate the relative energy ratio of each i(t)L, denoted as iE9: if Ei>ϵ then10:  y←y+ i(t)L11: end if12: end for13: Calculate PSE and SD of y using equations (13) to (17)14: Construct the eigenvector matrix, optimize the SVM modelOutput: Predicted and true value44.1After filtering, the decomposition of the original signal by improved EMD algorithm would generate a series of intrinsic modal functions and a residual signal, as shown in According to For the purpose of reconstructing the essential signals, IMFs need to be selected for this work. IMFs with high relative energy ratio contained most of information for original signal . Therefo4.2In this section, we drew images of reconstructed signal, the SD, and the PSE under working conditions S1, S2 and S3 respectively, as shown in It was obvious from In working condition S3, it was clear from 4.3From the analysis in Section 4.2, we known that the energy and frequency components of the vibration signal were different when the frame was under the three working conditions. It was directly reflected in the changes of the value of SD and the PSE. Its coordinated distribution is shown in Based on the above analysis, the SVM models based on the SD and the PSE were established. For illustrating the influence of the SVM model with different parameters on the recognition accuracy, we used particle swarm optimization algorithm, genetic algorithm, and grid search method to optimize the SVM model respectively. The optimal parameters and accuracy of different algorithms were shown in 4.4In order to illustrate the generalisation capabilities of the algorithm proposed in this paper, this section describes the applicability of the algorithm from different optimization methods, different machines and different crops.4.4.1When using particle swarm optimization algorithm, genetic algorithm and grid search method to optimize the SVM model, the actual and predicted data obtained were shown in As shown in When genetic algorithm was applied to parameter optimization, the S2 sample was incorrectly classified as S3, as shown in As can be seen from 4.4.2We also used the self-propelled 4YZLP-2C harvester to carry out the frame vibration test under the same working condition in this paper. PSE and SD were calculated after the obtained data was denoised, decomposed and reconstituted into a new signal, and then the feature vector matrix was imported into SVM and optimized by PSO. The recognition result was shown in 4.4.3The rice and wheat combine harvester is similar to the self-propelled corn harvester in the main structure. It is composed of an engine, a header, a frame, etc., and their field operation environment is very similar, resulting in vibration bending and torsional deformation of the frame. Thus, the vibration states identification method proposed in this paper also has certain applicability to the rice-wheat combine harvester.4.5In this paper, the improved EMD algorithm combined with the SVM was used to identify the vibration state changes of the frame under different working conditions. For the corn harvester, the measured vibration signal presented high noise and non-stationary characteristics caused by the vibration of field road bumps and fluctuations when walking in the field. If the characteristic values were directly extracted for recognition, the accuracy would be affected. Therefore, the EMD method was adopted to process the signals first. However, due to the phenomenon of mode mixing in the EMD algorithm, the original algorithm was improved to enhance the accuracy of decomposition before signal processing. Secondly, we decomposed the original vibration acceleration signal to obtain a series of IMFs, and selected several groups of IMFs according to the relative energy ratio to synthesize the new signal. Compared with the original signal, the new signal at this time has greatly reduced the interference of non-stationary and high-frequency noise, therefore it can better reflect the original vibration state of the harvester. Then we calculated the PSE and SD values of the new signal, and obtained the energy variation and frequency distribution of vibration under different working conditions. According to the combination of energy change and frequency distribution, we have analyzed the main excitation source and the change of vibration order under each condition. Finally, the feature vector matrix based on PSE and SD was constructed, and the model parameters of SVM were optimized. Through comparative analysis, the recognition accuracy of these three optimization methods is 99.21%, 98.68% and 97.37%, respectively.We could see that most of the signal information was contained in the IMF3, IMF4, and IMF5 after the original signal was decomposed when the machine was in the working condition S1. From these three signal patterns we could see that the vibration form was regular periodic vibration. As can be seen from frequency domain analysis, the components in the signal were only low frequency excitations from the engine, and the first and second order vibration was the main component. The frequency components of each order were far away from the natural frequency of the frame. In addition, the value of the SD was very small compared with S2 and S3, thus the overall vibration was relatively stable and the reliability was high. Most of the signal information was contained in the IMF3 and IMF4 when the machine was working condition S2, where the frequency components were dominated by the engine first-order ignition frequency and the low frequency excitation of the road. The main reason for this phenomenon was that as the engine speed increased, so did its higher frequency component, and it was easier to absorb by the ears in the grain tank. Hence, it could be seen from the spectrum that the frequency component of the higher order was much smaller than that of the lower order. Most of the signal information was contained in the IMF2 when the machine was in working condition S3. Similar to working condition S1, the vibration form at the frame was regular periodic vibration. With the increase of engine speed, the absorption of energy by the corn ear did not change much. In addition, the frequency division of the engine was very close to the second-order natural frequency of the frame , so reso5This paper proposed a novel vibration states identification method based on the improved emd and the SVM. Following conclusions can be drawn from the results of proposed methods:(1) The improved emd algorithm can effectively reduce noise interference and restore the effective information of original signal. IMFs generated by decomposition can better reflect the change of the single component for the original excitation signal.(2) The corn ear in the grain tank can absorb the vibration of the frame, and the absorption of vibration signal varies with the order. It reflected that it is not sensitive to the lower order signal components, but has a strong absorption effect on the higher order signal components.(3) The proposed method can accurately identify the vibration states of the frame, and its accuracy can reach 99.21%.However, there are still some limitations in this study: Firstly, the improved EMD algorithm still has mode mixing, and it may make IMFs lose its physical meaning and is not conducive to the later signal reconstruction. Secondly, the purpose of this study was to explore the vibration mechanism for the frame during the grain tank of corn harvester was in full load state, so other working conditions were not involved. Finally, compared with the traditional finite element analysis method, this paper did not concern the calculation of vibration mode and the damping ratio of structure. Thus, we will use mask signal method to reduce mode aliasing as much as possible in the future research. At the same time, the diversity of working conditions should be increased. Finally, we will consider identifying the parameters of the structure only from the output vibration response signal.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.JF: conceptualization, methodology, data curation, and writing—original draft preparation. RZ: conceptualization, resources, writing—review and editing, funding acquisition, and formal analysis. CC: resources, methodology, software, and project administration. ZC and DL: validation, data curation, writing—review and editing, and project administration. YQ: formal analysis and proofreading. All authors contributed to the article and approved the submitted version.


This paper discusses modeling the behavior and prediction of fracture of brittle materials. Numerous publications show that progress in this area is characterized by the emergence of a number of new models that meet the requirements of the mining industry, construction and other engineering practices. The authors focus only on one class of models, paying special attention to the compromise between simplicity of solution and versatility of the model. A new version of the model is proposed, taking into account the advantages of previous models. We present a differential energy criterion for brittle fracture substantiated, according to which, fracture occurs at a certain ratio of dissipated and stored (elastic) energy. Fracture is considered as the end of the deformation process with a virtual transformation of the initial material almost without cracks into a real material with cracks. The highest and lowest elastic moduli are analytically determined, respectively, on the ascending and descending branches of the stress–strain curve. A graphical version of the algorithm for determining the brittle fracture point on the post-peak branch of the stress–strain curve is proposed. The modeling results are consistent with the experimental data known from the literature. Rocks consist of many small mineral grains that form a strong but brittle framework in which intergranular boundaries, pores, cracks and joints are inevitable due to the conditions of origin and life cycle ,2,3. FroWe focus on the application of the energy approach in combination with Equivalent Material Concept (EMC) , which dNote that EMC and FMC are effectively used mainly for modeling load-bearing elements with cracks and notches of various shapes ,13,14. BThe use of EMC-based models has shown to be effective for small notch radii e.g., 0.15 mm), but predictions lose accuracy if the notch radius increases  mm, but  fills thAs the literature review has shown, the development of strength and fracture criteria that take into account the influence of all material defects requires further research, which is especially important for rocks because their physical and mechanical properties are very variable ,18. GiveTo make the following text easier to read, we have divided it into small sections, which are interconnected by the logic of the study. In addition, each important point of the study is explained by an example and comparison with the literature modeling results of some rocks. Thus, the logic of the study predetermined the form of its presentation in this article. The conceptual and methodological aspects of the study determine the way to achieve the goal, so these aspects are logically inseparable from the results. Nevertheless, the article is structured, and the above aspects and results of the study presented in separate, but logically related to each other small sections in order, as mentioned above, to facilitate the reading of the research material.As noted above, from the point of view of material mechanics, the heterogeneous structure of rock and similar materials is characterized by a nonlinear load–displacement relation. This dependence is an external manifestation of the internal processes of interaction of the aforementioned grains at the micro-, meso- and macro-levels, i.e., this dependence indirectly reflects all stages of rock behavior as a brittle material and is therefore of paramount importance for predicting the strength and fracture of rocks and similar materials ,5,6,7. TFrom the physical point of view, it is clear that under monotonic load, as the weakest particles and connections between them are gradually withdrawn from work, the cross-sectional area decreases and the load redistributed to those particles, which are still undestroyed. Therefore, the average stress in the undestroyed particles increases, leading to increasing deformations of the particles in accordance with Hooke’s law. This process is described in terms of weak link theory .Disconnection of particles and destruction of bonds between them means reduction in the cross-sectional area during loading relative to the initial value  by Given that θ defines the undamaged part of the original cross-sectional area; in physical terms, this function called a residual area function . After the residual life function (3), the damage measure (variable) defined as It follows from relation (4) that the function Note that various approaches to the definition of ll known ,27,28,29u. The estimates thus calculated refer to a specific specimen or to a specific structure (Equations (3)–(5) express estimates of the undamaged and damaged portions of the cross-sectional area using displacement tructure .In Equation (3), in order to characterize not only the structure but also the material, relative strain ( be used . If the The model parameter Using the above Using Equation (7), let us define the strain:Under uniaxial load Ratio (9) shows that the deformation of brittle material degrades the cross-sectional area .To continue the study, it is necessary to determine the dependence of force On the conceptual level, we assume that the average value of the elastic modulus of each individual particle, of which a brittle material consists, does not change under loading under normal conditions. This assumption does not contradict modern ideas about materials . This doUsing one of relations (7), let us express the modulus of elasticity from it and using Equation (3), we write:Equation (10) is acceptable for any pair of values of The values of the modulus of elasticity in relations (10) and (11) are the same, which makes it possible to obtain the equation for determining the dependence of the load The model parameter ogy with .In Equation (13), to characterize not only the structure but also the material, stress  :(15)σ=σpA comparison of Equations (14) and (15) shows that if We obtain more opportunities for nonlinear analysis if we use the Blagojevich model (16) :(16)σ=σpDDThe model parameter s method .By analogy with (16), we can modify model (14) to the form (17):Thus, model (14) and a modification of this model (17) represent two new steps in the progress of models of this class. The universality of models (14)–(17) is explained by the possibility of using them to independently control the pre-peak and post-peak branches of the full load–displacement (stress–strain) curve ,32. The Note also that Equations (14)–(17) are indifferent to signs of deformations, so they can be used for both compression and tension. Analysis of accumulated energy can be performed by integrating these equations. Thus, application of the analytical model (17) can improve understanding of the mechanical behavior of brittle materials, such as clarifying notions of the tangential modulus of elasticity in the pre-peak and post-peak stages, as discussed below.Analysis of brittle material state changes under its loading is a part of many research studies, the review and the current state of which can found in . BesidesUsing the second Equation (17), we define a function that models the dependence of elastic modulus on strain:The value of Using (19), we obtain two values of E and post-peak modulus Substituting given in ). In theIn this section, we will compare the results obtained with the proposed model (17) and experimental data for uniaxial compression of marble from  for two The Poisson’s ratio used in At the pre-peak stage of deformation , the iniSince the real material is inhomogeneous and has some plasticity potential, the load growth is accompanied by the interaction of two opposite tendencies, one of which is compaction and growth of elastic modulus and the other is growth of plastic deformations and gradual disconnection of the weakest links (in accordance with the theory of a weak link ). The inThe inflection points on the descending branch of the stress–strain curve for specimens 1, 2, 3 and 4 also indicated in The points with abscissa (20) and (21) determine, as indicated above, the largest pre-peak and smallest post-peak moduli . Thus, tVirtual transformation means the transition from the native material as it was at birth to the real cracked worn material. The observation mentioned in The tangent to the curve in dε) at the final stage of brittle fracture, i.e., we will use the differential approach to determine the “last straw” . For this reason, part of the input energy is dissipated. Another part of the input energy is stored in the undamaged deformed particles. The energy calculated as the area under the stress–strain curve, i.e., an integral approach is used. Note that such ideas about energy consumption during deformation of elastic–plastic and brittle materials are basic in a large number of studies ,39,40,41t straw” .The issue of the coordinates  is satisfied for real brittle materials:It follows from (22):Let us substitute From a physical point of view, the material is functional, i.e., the material resists the load if inequality (25) fulfilled. Therefore, inequality (25) used to construct a differential criterion for the strength of a brittle material .If strict inequality (25) is a condition of strength, then the condition of destruction is a non-strict inequality (26):The non-strict inequality (26) contains two conditions: According to the physical meaning of the problem, the fracture point on the descending branch of the stress–strain curve corresponds to the smallest value of ε . In thisGiven equality (22) and the failure condition (28), we obtain the following equations:The stress and strain at the fracture point  are denoUsing relation (17), we write the left part of Equation (32) in the form (33):Finally, using (32) and (33), we obtain Equation (34) to calculate The modulus of elasticity  on the tangent, draw a perpendicular to the abscissa axis. Find point 4.Define point 5 as the midpoint of segment 3–4.Draw a line through points 2 and 5. The point of intersection of this line with the post-peak branch of the stress–strain curve simulates the point of failure; this is point 6 in In the previous section, we considered an analytical model for determining the brittle fracture point. Considering the above, we can propose a simple algorithm for graphically determining the fracture point of a brittle material using a complete stress–strain curve :On the pAn example of applying the graphical determination of predicted fracture points for the four curves by The main result in this paper is models (14) and (17), which belong to the same class of models as the preceding models known from the literature ,31,32. Mdel from , which aIf ra model :(36)σ=σpThus, the known models explicitly consider only energy aspects or only deformation aspects. Given the heterogeneity of rocks, we can assume that the model will be more accurate if different aspects are taken into account. The proposed models (14) and (17) differ in the fact that the parameter The considered examples show the consistency of the simulation results with the experimental data.We considered the issue of analytical determination of the highest value of the modulus of elasticity. Equations (18)–(20) for calculating this modulus were obtained , which rThe above may be of interest for future research on this topic. First, the effect of material stiffness and load change rate on model parameters  machine ; however machine ,43,44.This article considers the approach to justification of the differential energy criterion of rock brittle fracture. It focuses on the conceptualization and application to the analysis of axial and lateral strain during uniaxial compression and fracture of rocks using marble and granite as examples. The study is based on previous results known from the literature.The hypothesis of the study was formulated (1): the damage to the cross-sectional area is proportional to the undamaged part of the area and to displacement (strain).In a logical connection with the research hypothesis (1), the residual resource function of the cross-sectional area (3) is justified.Based on the research hypothesis, new variants of load–displacement models (13) and stress–strain models (14), (17) were justified, taking into account both axial and lateral deformations .The question of analytical determination of the highest (pre-peak) and lowest (post-peak) values of the modulus of elasticity is considered. Equations (20) and (21) for calculating the coordinates and values (18) of these moduli are obtained. Examples of determining the pre-peak and post-peak modulus of elasticity in uniaxial compression of marble are given .An algorithm for the graphical determination of the brittle fracture point is proposed . An examGiven the small amount of research using the developed models, it is necessary to continue research in this direction, despite the consistency of simulation results and experimental data known from the literature.


Laser micromelting (LMM) technology allows for the remelting of pre-positioned coatings on the surface of a specimen to form a metallurgical bond with the substrate material, significantly improving the coating’s film–base bond. However, the high energy input from the laser modification process can cause severe element diffusion, rendering the coating susceptible to deformation and cracking. This can be mitigated by controlling the laser power, scanning speed, and offset of the LMM process. The temperature and stress fields of the samples in the LMM process were analyzed via finite element simulation. The effects of the LMM process parameters on the coating morphology were analyzed in conjunction with experiments. The results indicated that the laser power significantly affected the morphology of the coating after remelting, and a higher scanning speed was more likely to cause the coating to accumulate stress. Additionally, a smaller offset inhibited crack generation. At a laser power of 30 W, a scanning speed of 1200 mm/min, and a scanning spacing of 0.035 mm, the surface of the coating had no obvious defects and was relatively flat, and the adhesion and corrosion resistance were significantly improved. This study provides valuable guidance for improving the preparation of micron-sized protective coatings on Zr alloy surfaces. Dcoatings ,13. Coatcoatings ,15,16,17coatings ,19,20,21coatings ,23,24. Acoatings ,28,29,30The current protective coatings prepared on Zr alloy surfaces have the problem of low adhesion. Here, CrAl and Fe coatings pre-deposited on Zr alloys via PVD and electroplating were treated using LMM technology. The aim is to obtain a composite metal protective coating with high adhesion and precisely controllable thickness. Through this composite technology, the problems of the ferromagnetic material not being preparable via PVD technology and the non-uniformity of the coating prepared via electroplating technology are solved, and the adhesion force of the coating is greatly improved. The precise preparation of the composite metal coating with a controllable thickness and high adhesion at the micron level is realized. Ansys software (2020 R2) was used to simulate the temperature field and stress field in the LMM process, which solved the problem of undetectable changes in the amount of melting and stress between the coating and the substrate during laser processing. Combined with the experimental results, the effects of different process parameters on the coating quality were examined. A composite metal protective coating with high adhesion was successfully prepared by optimizing the laser processing process parameters. An improved strategy is provided for the preparation of composite metal protective coatings with high adhesion on Zr alloy surfaces.3 via wire-cut electrical discharge machining (WEDM). The purity of the CrAl alloy target  was 3N (Cr:Al = 73:27 at.%). The surface of the Zr alloy was sanded with 2000# sandpaper, ultrasonically cleaned with alcohol for 30 min, and rinsed with plasma-treated water. Finally, the sample was placed in a drying oven at 50 °C for 20 min to prepare the coating. A TSU-650 multifunctional coating machine  was used. Details regarding the deposition processes are presented in 2·4H2O), whose compositions are presented in A Zr alloy sheet (Zr-1Nb) was used in the experiments, which was processed into a standard specimen with dimensions of 20 × 20 × 5 mmThe samples were polished via WEDM. The morphology of the samples was analyzed using HitachiSU-8000 SEM , and the cross-sectional elemental distributions were analyzed using energy-dispersive X-ray spectroscopy (EDS). The physical phase of the coatings was analyzed using an X-ray diffractometer (XD-3) with a Cu anode target. The scanning speed was 4°/min, and the scanning range was 2θ. The coating bonding force was analyzed using a WS-2005 coating adhesion automatic scratch tester. The scratch length was 5 mm, the loading speed was 50 N/min, and the termination load was 50 N. A CS2350H electrochemical workstation was used to test the corrosion resistance of the coatings, and the experimental corrosion solution used was a 3.5 wt.% NaCl solution. The salt bridge was prepared using 30 wt.% KCl and 3 wt.% agar powder in a heated water bath. The auxiliary electrode was a Pt conductivity electrode, and a saturated calomel electrode was used as the reference electrode.3 and a fused cladding layer size of 20 × 10 × 0.02 mm3. The mesh was refined for the heat-source scanning area and its vicinity, and a coarse mesh was used at a further distance from the heat source. The temperature-field fusion cladding layer used a refined hexahedral Solid90 elements, the substrate used tetrahedral Solid87 elements, and the surface effect element was Surf152. There were 93,089 elements and 247,807 nodes in total. The model meshing diagram is shown in The variations in the temperature and stress fields during the LMM process were simulated using the Ansys software (2020 R2). Half of the samples were taken for modeling, where the thicknesses of the substrate and CrAl and Fe coatings were 5000, 7, and 13 μm, respectively. A symmetric model was created with a substrate size of 20 × 10 × 5 mm2) at the laser spot, A represents the absorption rate of the coating on the laser, P represents the output power of the laser (W), R represents the radius of the laser spot (m), v represents the scanning speed (m/s), t represents the scanning time (s), and  are the coordinates of the center of the spot at time t. A Gaussian surface heat source was used to simulate the LMM process, and the corresponding model is shown in ρ represents the material’s density (kg/m3), c represents the specific heat capacity of the material (T represents the temperature (°C).The enthalpy method was used in the simulations for the latent heat of the phase change. The enthalpy H is expressed asAccording to the thermoelastic-plastic theoretical mechanical model, an indirect coupling method was used to analyze the stress field. Cladding was applied using refined hexahedral Solid186 units. The displacement degrees of freedom in the stress field were constrained using displacement, which restricts translational motion along the X-, Y-, and Z-axes, that is, UX, UY, and UZ. The constraint action areas in those three directions were selected as the peripheral and bottom surfaces of the geometric model, and the model boundary constraints are shown in f is the dilution rate, h is the depth of melting of the substrate (µm), and H is the melt pool depth (µm). The amount of substrate melting was judged based on the position of the depth of the substrate’s melting point isotherm (The dilution rate of the substrate can intuitively respond to the performance of the molten layer; to illustrate the effect of different laser processing process parameters on the depth of the molten pool and metallurgical bonding, the expression of the substrate dilution rate is as follows:isotherm a. After isotherm b. Under 2 gives a good dilution rate.During laser processing, not only is a certain width of the melt pool required, but the depth of the melt pool must also exceed that of the coating to reach the Zr alloy substrate to form an effective metallurgical bond. However, it is necessary to prevent the Zr alloy substrate from melting excessively during the LMM process, which would lead to the diffusion of the Zr alloy into the coating surface and cause the coating to fail. As shown in 5 °C/s. The laser then moved away from the midpoint; the temperature continued to decrease and the cooling rate decreased. It can be demonstrated that the LMM process exhibited the characteristics of rapid heating and cooling, and the non-equilibrium solidification characteristic unique to laser processing promotes the optimization of the microstructure. Simultaneously, the rapid heating and cooling increase the coating stress, such that the coating is prone to cracking. α is as follows:When the scanning spacing was small, the overlapping region caused the coating to remelt twice, which improved the flatness of the coating. Moreover, the remelting zone’s structure remelted and condensed, promoting grain refinement and further releasing residual stress. Reducing the scanning spacing improved the flatness of the coating and inhibited crack formation. However, a scanning spacing that is too small results in severe diffusion of the coating elements.3 W/mm2, the demarcation line between the coating and the substrate was blurred and faded, and elemental Zr diffused throughout the coating, which led to the failure of the protective properties of the coating. With a further increase in the power density, the film–base bonding demarcation line disappeared. With an increase in the amount of the Zr matrix melted, the Zr content in the coating increased. According to the temperature field simulation analysis, the depth of the melt pool of the Zr matrix increased with an increase in the laser power and a reduction in the scanning speed, resulting in different degrees of Zr diffusion in the coating.The cross-sectional morphologies and elemental diffusion of the specimens subjected to the different processes are shown in According to the simulation and experimental results, a laser power of 30 W, scanning speed of 1200 mm/min, and scanning spacing of 0.035 mm, were selected to prepare the coating. The coating morphology was observed and its properties were tested.By combining the advantages of PVD and electroplating, an FeCrAl coating was pre-positioned on the surface of a Zr alloy, which was modified via an LMM treatment. The temperature and stress fields generated in the LMM process were analyzed using finite element simulations to narrow down the selection of the process parameters. Additionally, the effects of the LMM process parameters on the coating morphology were investigated. The results indicated that the laser power significantly affected the coating morphology and degree of elemental diffusion. Higher scanning speeds were more likely to cause stress accumulation in the coating, and the remelted coating overlapped poorly. Reducing the offset improved the coating flatness and effectively suppressed crack formation. With the LMM treatment, the coating and matrix elements diffused into each other, the film–base demarcation line disappeared, and a metallurgical bond was formed. At a laser power of 30 W, scanning speed of 1200 mm/min, and scanning spacing of 0.035 mm, the coating morphology was good and defects existing in the original coating were eliminated. Compared to the original coating, after the LMM treatment, the coating’s film–base bonding force increased by 56% and its corrosion resistance was improved. A corrosion-resistant coating with a strong bonding force was successfully prepared. This investigation provides valuable guidance for improving the laser modification of micron-sized protective coatings.