The current evaluation of M&A performance lacks consideration of M&A motives. In this paper, we theoretically analyse and empirically test the effect of network synergy generated by M&A on the degree of realization of corporate M&A motives and the mechanism of its effect by constructing an equity network between a listed company and its subsidiaries within the company. The results show that the greater the variation of internal network node degree and strength, the more beneficial it is to promote the degree of realization of corporate M&A motivation; the results of further mechanism of action tests show that the variation of network node degree and strength has significant effects on economies of scale, economies of scope, and transaction costs; Furthermore, the heterogeneity test finds that the effect of variation of network node degree and strength to promote the degree of realization of corporate M&A motivation is more significant in the case of non-cash payment method and related M&A. This paper extends the study of complex networks to the field of M&A and uniquely explains the paradox of the "high failure rate" of M&A and the increasing activity of M&A activities from the perspective of network synergy, which helps to rationalize the M&A behavior of enterprises and further regulate the M&A behavior of listed companies by regulatory authorities. Known as one of the most important strategic investment methods for enterprises to use the capital market to improve quality and efficiency, gain network synergies, and cultivate competitive advantages in the market, mergers and acquisitions (M&A) have had a significant impact on the development of a modern industrial system and on the high-quality development of the Chinese economy in recent years. According to the "Review of China’s M&A market in 2020 and prospect in 2021’ released by PwC", the deal value of M&A activities in China increased by 30% in 2020 to reach $7,338 billion US dollars, the highest level since 2016. However, scholarly research indicates that the growing M&A activity is accompanied by a high rate of failure .The mainstream approach to evaluate M&A performance is to compare cumulative abnormal stock returns, long-term shareholder value, and changes in financial metrics before and after the acquisition. However, these approaches may overlook the "original intent" of M&A motives and result in inaccurate conclusions. Several Scholars attempted to evaluate M&A performance from the perspective of M&A motives, and Brouthers et al.  further Under the background of the new era, enterprise networking features become increasingly distinct, and competition among businesses have transformed into competition among businesses’ economic networks. The enterprise economic network is a complex system jointly formed by an enterprise’s internal economic relationship based on control relationships and inter-firm economic networks embedded in enterprises. Since M&A is the merging and rearrangement of two economic systems, and the network economy can modify the structure of the enterprise economic network before and after the merger to build a network economy, the network economy becomes the most important source of synergy effects of M&A in the enterprise economic network. However, in recent years, goodwill has been frequently “mined” by companies, including failures to meet performance expectations for high premium M&A and high failure rates for M&A. The reasons for these phenomena include, but are not limited to, insufficient understanding of the economics of corporate M&A and insufficient network economy theory and applied research to clearly guide the practice of business combinations. Therefore, it is a great theoretical and practical significance to investigate the influence of network synergies generated by changes in the acquirers’ economic network before and after mergers and acquisitions on the realization of M&A motivations.The purpose of this paper is to examine whether network synergy has an impact on the degree of M&A motivation realization from the perspective of network node degree and strength change in M&A affairs occurring in China’s capital market. Its aims to provide theoretical and empirical evidence on the rationality of the degree of M&A motivation as a criterion for determining the success or failure of M&A. (2) To analyze and explain the relative contribution of network synergy on the degree of M&A motivation. (3) To guide M&A practice and provide useful theoretical support to further regulate corporate M&A behavior.The remainder of this paper are organized as follows: Section 2 conducts theoretical analysis and develops our research hypothesis. Section 3 describes our empirical models. we present the date source, sample selection, and the main empirical results of the simulations and give some discussions in the fourth section. Finally, we show the relevant conclusions and the outlook of this work in the fifth section.For Chinese businesses operating in a transitional economy and a traditional relational society, the informal system of social networks is critical for corporate governance and resource allocation. The application of social networks in enterprises promotes the establishment and development of economic networks. Through interconnection, businesses build networks to acquire scarce resources such as knowledge and information through a collection of mutual relationships , 13. TheMergers and acquisitions generate network synergies by changing the topology of economic networks. The synergistic effect of M&A is considered to be the "1+1>2" effect of the matching relationship between the merging parties. In other words, the overall performance of the group of firms is better than the simple sum of the performance of the individual parts. To achieve synergies in a business acquisition, the acquirer must either be able to limit the threats of existing and potential competitors in input markets, production processes, or output markets; or it must be able to develop new markets or encroach on competitors’ markets, rendering competitors unable to respond . M&A is The network synergies generated by M&A can have a significant impact on the realization of M&A motives. In the context of the "new normal" of China’s economy, explicit M&A motivation is crucial for firms to acquire innovation capabilities, which directly affects the realization of M&A strategies . CurrentHypothesis 1: The network synergy generated by M&A can improve the performance of the firm.The network synergy generated by M&A can produce a "resource effect" that will enhance an enterprise’s resource endowment and ability to utilize resources, generate economies of scale and scope, and reduce internal and external transaction costs. According to resource-based theory, a company will obtain more resources and capabilities if it has a unique network structure. The subjects in the nodes of the relationship network, which are also called "social knots" in the network, can use the linkage paths in the network to exchange and disseminate information. The closer to the center of the network, the more useful information can be accessed, and the more breadth and depth of information can be obtained . TherefoHypothesis 2–1: The network synergies generated by M&A can enhance economies of scale.By establishing inter-firm connections, firms can break down information barriers and effectively help them gain access to a variety of complementary resources that are not available internally –29. CompHypothesis 2–2: The network economy generated by M&A can enhance the economy of scope.According to institutional economics, enterprise networks, alongside market and bureaucracy, is one way of resource allocation. The enterprise network is comprised of nodal firms that are linked by complementary strengths and resource sharing –33. ThroHypothesis 2–3: The network synergies generated by M&A can reduce transaction costs.This paper selects listed companies in Shanghai and Shenzhen A-shares that underwent mergers and acquisitions from 2007–2019 as the research sample, and the required data are obtained from the Guotaian database (CSMAR) and the Wind database (Wind). The selection criteria are as followed: (1) exclude M&A companies treated by ST, *ST, and PT; (2) exclude listed companies in the financial and insurance industries; (3) exclude samples of asset divestiture, asset replacement, debt restructuring, share repurchase and other types of restructuring; (4) retain the sample of M&A companies whose acquirers are listed companies and select only those with the M&A progress mark "completed "; (5) exclude samples with M&A amounts less than 1 million yuan; (6) for the same company with multiple M&As completed in the same year, retaining the first M&A event completed by the company in that year to reduce the interaction between different M&A events; (7) exclude samples with incomplete key data; (8) Winsorize the tails of all continuous variables at the 1% and 99% quantiles to avoid the adverse effects of some samples with extreme values on the empirical results. After the above screening, a final sample of 2201 M&A is obtained.The main regression test models are:In the model, Realization is the dependent variable that measures the degree of realization of the M&A motives, and ΔROE is used to examine the change in performance of the acquirer after the M&A. The difference between the acquirer’s ROE value in the year following the completion of the M&A (the first and second years) and the acquirer’s ROE value in the year preceding the completion of the M&A is used to determine the degree of realization of the M&A motive.The variation of node degree (N) and strength (S), referred to ΔN and ΔS, in the internal and external networks of listed companies are proxy variables for network synergy. The node degree (N) refers to the sum of the number of nodes connected to a node, which reflects the node’s ability to obtain resources in the network. In general, the higher the node degree is, the stronger the "intermediary role" of the node in connecting other nodes in the complex network.j∈k aij. Therefore, the calculation of change in node degree ΔN before and after the merger is:The calculation of node degree is Ni = ∑ΔN = (node degree of listed companies in the year of M&A completion—node degree of listed companies in the year before M&A completion) /nodal degree of listed companies in the year before M&A completion.The node strength (S) refers to the sum of all edge weights connected to the nodes, reflecting the closeness of cooperation with other subjects, and is an important characteristic variable for establishing cooperation and communication between enterprises and other network subjects –41. The j∈k wij. Therefore, the calculation of change in node strength ΔS before and after the merger is:The calculation of node strength is Si = ∑ΔS = (node strength of listed companies in the year of M&A completion–node strength of listed companies in the year before M&A completion) /node strength of listed companies in the year before M&A completionThe reason for using proportional calculation is that the change in the acquirer’s position in the network depends on its initial position. For example, a decrease in node degree of 0.25 is more meaningful for a company with an initial node degree of 0.5 (50% change) than for a company with an initial node degree of 0.75 (33% change).This paper constructs an equity network formed by a listed company and its subsidiaries, so the node degree is the number of the listed company’s subsidiaries and the node strength is the sum of the listed company’s shareholdings in subsidiaries.Table 1.Referring to the studies of Chen et al. , Liu et Table 2 illustrates the M&A transaction characteristics of the sample. following Panel A, cash payment M&As accounted for 63.83%, and non-cash payment M&As accounted for 36.17%, indicating that cash payment is the main payment method in the Chinese M&A market; related-party M&As accounted for 35.85%, and non-related M&As accounted for 64.15%. M&As of asset restructuring accounted for 28.62%, and M&As of non-major asset restructuring accounted for 71.38%; The proportion of equity bids in M&A targets reached 94.91%, and the proportion of non-equity bids was only 5.09%; among the types of M&As, horizontal mergers, and acquisitions accounted for 10.36%, vertical mergers and acquisitions accounted for 54.70%, accounting for more than half of the total sample, mixed mergers accounted for 29.53%, and other types of mergers accounted for 5.41%. Panel B illustrates the stock market and industry distribution of the sample. Based on Table 2, M&A in the main board market accounted for 39.48%, and M&A in the SME board and GEM board accounted for 60.52%; the manufacturing industry had the largest sample with 66.79%, M&A in the information transmission, software, and information technology service industry accounted for 8.91%, and M&A in the wholesale and retail industry accounted for 4.50%.Panel A in Table 3 shows the descriptive statistics of the main variables in the model. The results show that in the equity network, the means of the degree of realization of M&A motivation in the year of M&A completion, one year after completion, and two years after completion are -0.0004, -0.013, and -0.037 respectively, which indicated that the M&A motives of the companies in the sample are generally not realized, and the M&A has not improved the performance level of the enterprises. The mean of the changes in the node degree of listed companies is 0.6890, indicating that the number of subsidiaries of listed companies after the completion of M&A is greater than the number of subsidiaries of listed companies before M&A. The mean of the changes in the strength of listed companies is 0.7190, indicating that the strength of control of listed companies over subsidiaries after the completion of M&A is greater than the strength of control of listed companies over subsidiaries before M&A.Table 4 shows the correlation analysis of the main variables. Overall, there is no serious collinearity problem among the control variables.Table 5. Firstly, the effects of changes in node degree ΔN and changes in strength ΔS of listed companies on the degree of realization of corporate M&A motives are examined individually, shown in columns (1) and (5). Secondly, the effects of changes in node degree ΔN and changes in strength ΔS of listed companies on the degree of realization of corporate M&A motives are examined jointly with the control variables, shown in columns (2) and (6). Thirdly, the effects are examined again by controlling Industry and Year, shown in columns (3) and (7). The last one examines the effects of adopting cluster analysis on Industry, shown in columns (4) and (8). The results show that the change in node degree (ΔN) and the change in node strength (ΔS) have a significant positive effect on the degree of realization of corporate M&A motivation  in the year in which the M&A is completed.This section examines the effects of changes in the node degree and strength of the firm’s internal network on the degree of realization of the M&A motives. The regression results for the year in which the M&A was completed are shown in Table 6. The results show that after controlling for year and industry and clustering the industry, the change in node degree of listed companies (ΔN) has a significant positive effect on the degree of realization of corporate M&A motivation  one year after M&A completion. Under the univariate tests, the change in node strength (ΔS) of listed companies positively affects the degree of realization of corporate M&A motivation  one year after M&A completion, and it is statistically significant at the 10% level of significance. After controlling for year and industry and clustering the industries, the change in node strength (ΔS) of listed companies still has a positive effect on the degree of realization of corporate M&A motivation  one year after M&A completion, and it is also statistically significant at the 10% level of significance.The regression results for one year after the completion of M&A are presented in Table 7. The results show that the change in the node degree of listed companies (ΔN) and the change in node strength (ΔS) have a negative but insignificant effect on the degree of realization of corporate M&A motivation  two years after the completion of M&A. The results for the year of M&A, the year after 1 year, and the year after 2 years show that the effect of network economic synergy becomes less pronounced as the integration of M&A takes longer. Overall, combining the regression results of the year of M&A completion, one year after, and two years after, the internal network economy generated by M&A can improve M&A performance and promote the realization of corporate M&A motives, therefore, the results are consistent with the Hypothesis 1.The regression results for 2 years after the completion of M&A is presented in Table 8 examines the impact of M&A payment methods on corporate M&A performance. The results show that there is a significant difference in the impact of network synergy on the degree of M&A motivation realization under non-cash payment and cash payment, with the use of non-cash payment method (mainly reflected in share-based payment M&A), the change in nodal degree (ΔN) and the change in node strength (ΔS) significantly contribute to the degree of realization of corporate M&A motivation and are statistically significant at the 1% level. It indicates that the original shareholders of the target company will become strategic investors, which makes the benefit bundling mechanism between the M&A parties stronger and the network economy has a greater impact on the degree of M&A motivation realization. When cash payment is used, the change in node degree (ΔN) and the change in strength (ΔS) have a negative but statistically insignificant effect on the degree of realization of corporate M&A motivation.Table 9, the internal network changes resulting from M&A in the sample of connected transactions significantly contribute to the degree of realization of corporate M&A motives, and the results are statistically significant. This indicates that there are fewer corporate cultural differences between the M&A subjects that have related transactions, which facilitates the smooth transfer of information between the acquiring parties and faster realization of M&A synergies to achieve the realization of M&A motives [The related transaction also impacts the M&A activities significantly. As shown in  motives , 46. HowAppendix 1, 2 in Robustness test), and the results are still consistent with the hypothesis.Under this test, the regressions are conducted using ΔROA and ROA in the year of M&A completion as substitute variables for the degree of realization of corporate M&A motives respectively, and the remaining variables are kept constant. The regression results are shown in Appendix 3 in Robustness test), and the results still support the hypothesis.The control variables of M&A transaction size and payment method are added to test the effects of changes in the node degree (ΔN) and changes in node strength (ΔS) of listed companies on the degree of realization of corporate M&A motives. The regression results are shown in To test the impacts of network synergy effects on economies of scale, economies of scope, and transaction costs due to changes in economic network nodes, models (3)-(8) are constructed to test hypotheses H2-1 - H2-3:For the measure of economies of scale , referring to the study of Zhang and He , after MScale = Proportion of the annual procurement amount of the top five suppliers in the year of completion of the merger and acquisition—Proportion of the annual procurement amount of the top five suppliers in the year before the completion of the merger and acquisitionIf the result is positive, it indicates that the dependence of the company on the supplier increases after the completion of the M&A. If the result is negative, it indicates that the dependence of the company on the supplier decreases, and the scale effect increases. The greater the decrease in this value, the greater the scale effect.The economy of scope (Scope) in this paper is measured by using the revenue Herfindahl index and the revenue entropy index, which are widely used in the existing literature. The entropy index is calculated based on the proportion of revenue from each industry to total revenue based on the “Industry Classification Guidelines for Listed Companies revised by the China Securities Regulatory Commission” in 2012, the Wind database, and the segment reporting information disclosed in the annual reports of enterprises. The income Herfindahl index refers to the squared sum of the proportion of each industry’s income to the total income of the enterprise, and the larger this index is, the less diversified the enterprise is.Therefore, this study uses the change in entropy index and Herfindahl index before and after the M&A, which is calculated as follows:Scope1 = Entropy index in the year of M&A completion -Entropy index in the year before M&A completionScope2 = Herfindahl index in the year of M&A completion—Herfindahl index in the year before M&A completionFor the measurement of transaction cost savings, Ang et al.  used theCost = Selling, administrative and financial expenses as a percentage of total assets in the year in which the acquisition is completed—Selling, administrative and financial expenses as a percentage of total assets in the year before the completion of the acquisitionTable 10.The regression results of the effects of changes in node degree (ΔN) and changes in node strength (ΔS) on economies of scale, economies of scope, and transaction costs in the economic network of listed companies are shown in Columns (1), (3), (5), and (7) show that the change in the node degree (ΔN) of listed companies significantly impacts the economies of scale, economies of scope, and transaction costs, where the change in the node degree (ΔN) of listed companies promotes economies of scale and scope and reduces transaction costs. On other hand, Columns (2), (4), (6), and (8) show that the change in node strength (ΔS) of listed companies promotes economies of scale and scope but has no significant effect on transaction costs.This paper constructs an equity network between listed companies and their subsidiaries, theoretically, analyses, and empirically tests the impact of network synergy on the degree of realization of corporate M&A motives, and draws the following conclusions using M&A activities in Shanghai and Shenzhen A-shares as a research sample from 2007 to 2019. (1) Mergers and acquisitions, as a method of merging and reshaping two economic networks, can lead to changes in the degree and strength of internal and external network nodes of enterprises. Generally, the greater the changes, the greater the network synergy effect it generates, which is conducive to promoting the realization of enterprises’ M&A motives. In non-cash payment methods and related M&A, changes in the degree and strength of network nodes have more significant effects on the degree of realization of corporate M&A motives. The network synergy generated by M&A can significantly enhance M&A performance, generate economies of scale and scope, and save transaction costs.This paper provides a novel explanation for the frontier issue of the paradox of M&A’s "high failure rate" and increasing activity. By organically linking M&A motives and M&A network synergies to explain M&A behavior and effects, it provides new evidence for established studies to judge M&A success or failure in terms of whether M&A motives are realized. Moreover, it enriches the existing research on factors influencing the realization degree of M&A motivation. Based on the changes in the nodal degree and strength of corporate economic networks, a complex systems approach is used to construct a model to examine the relative contribution of M&A synergies on the realization degree of M&A motivation, forming a useful addition to the existing literature.The findings of this study helps enterprises planning to implement M&A strategies to clarify their M&A motives, make rational judgments on network synergies, scientifically carry out M&A valuation and avoid excessive M&A premiums. Meanwhile, it helps the relevant regulatory authorities to formulate policies for further regulating the M&A behavior and disclosing M&A-related information of listed companies. Additionally, the findings are conducive to the formation of a modern industrial system and the high-quality development of China’s economy as well. Furthermore, it will facilitate the formation of macroeconomic networks, including the Eurasian "connectivity strategy", and the proper implementation of M&A activities of group companies.Due to the limitations of information disclosure and data acquisition, the economic network constructed in this paper is based on the formal relationship between enterprises. However, the informal relationship between enterprises may also play an important role in the strategic practice of enterprises. Future research can consider building an economic network based on the informal relationship between enterprises. In addition, for the sake of simplifying the model and highlighting the research focus, the relevant factors affecting enterprise network collaboration in the model may not be comprehensive enough. Subsequent research can consider the degree of realization of M&A motivation based on the impairment of M&A goodwill, and further, deepen the research on the intermediary effect of the impact mechanism.The increasingly active M&A activity is accompanied by the "high failure rate". This paradox has become one of the problems that has long puzzled the theoretical and practical fields . Some scLearn from the previous research, this study constructs an equity network to explain the realization of M&A motivation and the complicated mechanism that why the realization is affected based on the idea of network synergies. The results show that the greater the variation of internal network node degree and strength, the more beneficial it is to promote the degree of realization of corporate M&A motivation. Furthermore, the network synergy generated by M&A can significantly enhance M&A performance, generate economies of scale and scope, and save transaction costs.Based on network collaboration, this paper uses the realization degree of achievement of M&A motivation as the criterion for determining M&A success or failure, which not only expands the research field of complex networks, but also reasonably and uniquely explains the paradox of "high failure rate" and increasingly active M&A activities from a new perspective. This makes up for the shortcomings of existing studies that commonly used M&A performance indicators and their measurement methods may be detached from the M&A motivation, resulting in biased judgment results. Furthermore, we adopt a large sample statistical analysis method to measure the relative contribution of M&A synergy to the degree of M&A motivation, which enriches the research results on the factors influencing M&A motivation.As a basic theoretical research result, this study is conducive to guiding M&A behavior to be more rational, and also to guiding M&A application research. As stated in the article, although the paper has achieved certain theoretical contributions and practical implications, there are still limitations and shortcomings that need subsequent improvement.S1 Dataset(XLSX)Click here for additional data file.S1 Appendix(DOCX)Click here for additional data file.


The mean values of U2 RL  and U3 RD (canine root dilaceration angle) were statistically significantly lower in the BC and PC groups. Conclusions: The results of this study suggest that shorter root lengths and increased angulation values may lead to the ectopic eruption of canines. The higher canine angulations in the PC group increase the degree of root resorption.Background: The prognosis of orthodontic treatment for a patient with impacted canine teeth can be affected by many factors and understanding some of the characteristics of impacted teeth can increase the effectiveness and reduce the duration of treatment. This study aims to explore the effects of positions and dentoalveolar morphological characteristics of impacted maxillary canines on the prognosis of orthodontic treatment. Methods: In this study, forty-six adolescent individuals who applied for treatment of impacted teeth were divided into three groups: CC (no impacted maxillary canine group), BC , and PC . The impacted canine and adjacent teeth were evaluated in terms of position, angulation, inclination, mesiodistal and buccolingual widths, root length, dilaceration angle, and root resorption. Results: The mean values of U3 Ang (angulation of the maxillary impacted canine) and U3/U2 angles  were statistically significantly higher in the BC and PC groups ( Maxillary canines show eruption abnormalities characterized by frequent ectopic eruptions or impactions because they are between teeth that erupt later than other teeth in the dental arch and follow a long and difficult eruption pathway higher than the other teeth during the eruption. Knowing the basic concepts of development and eruption will help to understand the cause of malposition and choose the appropriate treatment ,2.Maxillary canines are the most commonly impacted teeth in the dental arch, following the third mandibular molars. Dachi and Howell conducted a study on full-mount radiographs from patients over the age of 20 and found a 0.92 incidence of impacted maxillary canines. They stated that these cases were mostly unilateral and saw twice as many women as men. The incidence of mandibular canines was approximately 0.09 .Two major theories (genetic theory and guidance theory) explain the cause of the ectopic persistence of permanent maxillary canines. According to the guidance theory, the roots of the maxillary lateral incisor guide the eruption of the maxillary permanent canine in the dental arch in the proper direction. The canine is guided by the roots of the previously erupted permanent laterals to its normal position. In cases such as hypoplasia or lateral incisor aplasia, the canine crown cannot continue in its proper place and becomes palatal dysplasia. According to the genetic theory, ectopic eruption, or impaction of the canine in the palatal direction, is due to genetic reasons. In ectopic eruptions or impaction due to genetic reasons, the same problems can be seen in other family members ,4,5,6.The mesiodistal localization and angulation of the canines are important regarding their eruption potential. Ericson and Kurol reported that permanent canine crowns positioned mesially are less likely to erupt after the extraction of deciduous canines. The proportion of impacted canines in the buccal/labial direction is less than that of impacted canines in the palatal direction. Impacted maxillary canines are 85% palatal and 15% labial/buccal . During Cone beam computerized tomography (CBCT) is a more accurate method, in comparison to traditional radiographs, for defining the position of the impacted canine in three dimensions and analyzing whether there is root resorption . In addiTreatment of impacted canines often involves a complex multidisciplinary approach that requires surgery, periodontology, restorative treatment, and orthodontic applications. A clinical and radiological evaluation must be assessed on a patient with an impacted canine to fully evaluate the existing malocclusion and the prognosis of orthodontic treatment for the location of the impacted tooth. Age, cooperation, oral hygiene, skeletal variation, spacing or crowding in the dental arch, and the vertical, anteroposterior, and transverse positions of the canine crown or root influence the prognosis. The prognosis is poor if the canine is placed close to the midline and at an angle of more than 45° to the midline. Furthermore, for successful treatment, the root should not have ankylosis or dilaceration and should be carefully examined to determine any resorption of the incisor roots. The more the tooth is moved, the poorer the prognosis. A good buccal overjet-overbite relationship and a correct root position ensure a stable outcome ,14,15.Furthermore, if the canine is above the occlusal plane, the prognosis of treatment is poor. McShery defined the “vertical rule of thirds” to determine the vertical level of impacted canines. He stated that if the apex of the canine tubercle is at the level of the adjacent incisor’s enamel-cementum junction, the prognosis of treatment is good. The prognosis is moderate at half the length of the incisor root and poor at the apical third . If the Pitt et al. investigated the factors affecting the difficulty of treatment. They found that the ectopic canine’s horizontal position, the age, the BL position, or the vertical height of the ectopic tooth are among the factors affecting orthodontic treatment .Root resorption was observed in 38 percent of the laterals and 9 percent of the central incisors due to ectopically located canines, but only 3 percent of normal erupting canines had root resorption in the lateral incisors. Once resorption occurs, it progresses quite rapidly. Therefore, the diagnosis and treatment decisions should be made very quickly and resorption occurs more frequently in palatal canines . When imKnowing some of the characteristic features of impacted teeth may increase the effectiveness of treatment and shorten the duration. This study aims to explore the effects of vertical, horizontal, and transversal positions and the dentoalveolar morphological characteristics of impacted maxillary canines on the prognosis of orthodontic treatment, and additionally detect the existence of crown root anomalies or resorption that may occur in impacted teeth or adjacent teeth. The current study’s null hypothesis is that no statistically significant differences in morphological features exist between the unilaterally impacted maxillary canines and the nonimpacted canines.The project was approved by the ethics committee of Ankara Yildirim Beyazit University, Ankara, Turkey (ref: 08.12.2022/19-1272).This study was planned as a cross-sectional study. CBCT images of 46 patients with impacted canines who applied to the Department of Orthodontics, Faculty of Dentistry of Ankara Yildirim Beyazit University for the treatment of impacted teeth were used. All CBCT images were taken with a Promax 3D Mid machine . The optimum exposure parameters 94 kVp, 14 mA, and 27 s were used for exposure. A single orthodontist evaluated the CBCT images in 3 sections . All angular and dimensional measurements were made using the Planmeca Romexis 3D Imaging Program .The G*Power software was used to calculate the sample size . The power analysis was calculated with reference to the angle of the impacted canine to the midline, as evaluated in the study by Al-Tawachi et al. . Accordiadolescents aged 10–16;unilateral impaction of permanent maxillary canines;presence and eruption of the maxillary lateral incisors;CBCT images with good image quality.Inclusion criteria:no systemic or dentofacial deformities;without syndromic or cleft palate;without orthodontic treatment;no contributing history of trauma;no multiple missing teeth;no presence of cysts or other pathology.Exclusion criteria:no impacted maxillary canine group ;unilateral buccally positioned maxillary impacted canine group (BC);unilateral palatally positioned maxillary impacted canine group (PC) .The samples were divided into three groups based on the location of the impacted canine:The demographic characteristics of the participants (age and gender) were identified. The following canine variables were measured: side of impaction (left or right); location ; rotation ; level of contact relationship with the adjacent tooth ; impacted canines with the sagittal view (sectors 1–5); root resorption levels and number of adjacent teeth (grade 0–3).Sector: some reference planes are used to evaluate the sagittal position of the canine and the amount of overlap (sectors) with other teeth. These are divided into 5 categories: 1 = in normal position; 2 = to the plane passing through the mid-long axis of the lateral incisor; 3 = to the plane tangent to the mesial part of the lateral incisor; 4 = to the plane passing through the mid-long axis of the central incisor; 5 = to the plane tangent to the mesial part of the central incisor  Figure .Resorption: the resorption in the adjacent teeth was evaluated using axial CBCT images. The Ericson–Kurol classification was used to grade the degree of resorption. Grade 0 (no resorption): intact root surface except for cement loss; grade 1 (mild resorption): mild resorption of the pulp up to half the thickness of the dentin; grade 2 (moderate resorption): half or more of the pulp is covered with pulp dentin; grade 3 (severe resorption): the pulp is exposed .The angulation of the maxillary impacted canine (U3 Ang), the lateral incisor (U2 Ang), and the first premolar (U4 Ang) is the angle between the vertical line and long axis line of the lateral incisor, the canine, and the first premolar. The angle between the impacted canine and lateral incisor (U3/U2) is the angle between the long axis of the impacted canine and the long axis of the adjacent lateral incisor .The buccolingual (BL) and mesiodistal (MD) widths of the crowns of the lateral incisor (U2) and canine (U3) on the sagittal and coronal sections were measured from the widest point of the crown perpendicular to the long axis .Canine root length (U3 RL) and lateral root length (U2 RL) were measured from the crown-root junction to the root apex, and the total length  of the lateral incisor and canine was measured from the incisal crown tip to the root apex on the sagittal view . The horThe inclination of the maxillary impacted canine (U3 Inc) and lateral incisor (U2 Inc) is determined by measuring the angle between the plane passing through the long axis of the lateral incisor and canine and a vertical plane in the sagittal section. . The roop < 0.05 was considered significant.The data were analyzed using the SPSS program . In the study, the analysis of the normal distribution of the measurements was performed using the Shapiro–Wilk test. Mean ± SD, which are descriptive statistical data for the variables, are given in This study consisted of 22 males (47.8%) and 24 females (52.2%). The mean age of the included subjects was 14.58 ± 2.29 years. The side of impaction showed that 23 (50%) of the impacted canines were located on the left and 23 (50%) on the right side . The dem2 = 6.02, p < 0.05) was found between the groups. The impacted canine rotation was found to have the highest percentage of mesiopalatal rotation (54.3%). Distobuccal and distopalatal rotations were not observed. Distributions of contact relations, location, rotations, sectors, and severity of resorption are analyzed by the chi-square test  and are presented in 2 = 48.08, p < 0.01) was found between the groups  of the forty-six impacted canines were vertical, while four (8.7%) were horizontal. A statistically significant positive association  (The highest percentage was observed in Sector 1 (45.7%), and the lowest percentage in Sector 4 (0%), Sector 3 (13.3%), and Sector 5 (13%). Although sectors 1 and 2 had a higher percentage in the buccal-impacted group, sectors 3 and 5 were found to have a higher percentage in the palatal-impacted group (c < 0.01) .2 = 48.80, p < 0.001). The overall prevalence of root resorption was 56.5%, as presented in 2 = 33.84, p < 0.001) was found between the groups (The contact relationship was highest at the crown level (32.6%) and lowest at the apical level (17.4%) (ce groups .p < 0.001). Similarly, U3/U2 values showed significant variations. The mean values of the angulation of U4 were significantly higher in the BC group than in the CC group (p < 0.05)  .p < 0.05) and PC group (p < 0.001) than in the CC group. The mean values of U3L were found to be statistically significantly lower in the PC groups compared to the BC group (p < 0.05).In the mean values of U3 L, U2 L, U3 RL, and U2 RL, significant differences were found between the PC and CC groups. The mean values of U2 RL were statistically significantly lower in the BC group (p < 0.001). The mean values of U3 HD were found to be lower in the PC group compared to the BC group (p < 0.001).There were differences in the mean values of U3 VD and U3 HD between the BC and CC groups, and between the PC group and the CC group (p < 0.01) and PC groups (p < 0.001), compared to the CC groups. The mean values of U3 RD and U2 RD were found to be lower in the BC (p < 0.05) and PC groups (p < 0.001) compared to the CC group. However, similar values were observed for the mean U4 RD angle values between the groups.The mean values of U3 Inc were found to be statistically significantly higher in the BC  was higher than the buccal-impacted canine group (26.23). In terms of the resorption rate and canine angulation, the severe resorption rate was found to be higher in the palatal group (grades 2 and 3). Long-term follow-up of teeth with root resorption in the adjacent region due to ectopically erupted canines showed no problems. In most of these cases, either the resorption regresses spontaneously or after surgery. Resorption decreases when the ectopically erupted canine is removed, or its position is changed by orthodontic treatment .Al-Tawachi et al. reported that the root lengths of the lateral incisors in the palatal-impacted canine group were shorter than in the buccal-impacted and control groups, while the mesiodistal and buccopalatal widths were similar in all groups. In addition, they said that the lateral incisors were inclined distally and palatally . Koral eBL and MD width of the lateral incisors in the PC group were smaller than in the other groups. However, the U2 RL and U2 L were smaller by 0.82–2.31 mm compared to those in the CC group and smaller in the PC group compared to the BC group, and the study’s hypothesis was rejected. These findings are also consistent with previous research indicating that the length of the lateral incisors is shorter on the impacted side ,38,39,40The limitations of the present study include its retrospective design at a single center. In this study, we could only include CBCT images that fully captured the maxillary region. The findings of this study may be helpful in facilitating the diagnosis of maxillary impacted canines, preventing problems, and planning effective treatment. More research is needed to evaluate the angular and linear measurements on CBCT images in a wider population. One of the limitations of this study is that confounders were not included in the analysis. Adjusting for confounders in future studies will make the conclusions more robust.As technology develops, future studies may apply artificial intelligence to various imaging modalities to provide information on predicting the maxillary canine impaction and treatment prognosis.The lateral root length adjacent to the impacted canine was shorter, and the root dilaceration angle was smaller ,39. SimiAs described in the eruption guidance theory, the roots of the lateral incisors guide the erupting permanent canines. In this study, it is clearly seen that the lateral incisor length and lateral incisor root length were found to be short in the BC and PC groups, which supports this theory. In other words, it should be considered that lateral incisors with shorter root lengths may be a potential cause for the maxillary canine impaction.The maxillary impacted canines are more vertically positioned, mesiopalatally rotated, and in contact with adjacent teeth at the crown level.Palatally impacted canines are horizontally closer to the midline and vertically higher from occlusion. Buccally impacted canines are positioned away from the midline and vertically lower. In addition, canine angulations are higher in the palatal group, making them more difficult to treat and increasing the degree of root resorption. Considering the possibility of resorption in these teeth in the clinic, orthodontic treatment that does not increase resorption with light forces should be applied.The widths of the lateral incisor crowns were smaller in the palatally impacted canine group, although there was no statistically significant difference. These findings are one of the potential predictor factors indicating that the canine may remain impacted according to the guidance theory.The fact that the U2 RL and U2 L values in the PC were lower than those in the BC suggests that the reduced dimensions of the maxillary lateral incisors and shorter roots may be a potential factor in the impaction of the maxillary canine.


Solanum nigrum. Using the peak signal-to-noise ratio (PSNR), structural similarity (SSIM) and mean opinion score (MOS), the models were evaluated by the image effects after reconstruction and were applied to the recognition of endocytic vesicles in Solanum nigrum root cells. The results showed that the single reconstruction and the secondary reconstruction of SRGAN and SRResNet improved the resolution of laser confocal images. PSNR, SSIM, and MOS were clearly improved, with a maximum PSNR of 47.690. The maximum increment of PSNR and SSIM of the secondary reconstruction images reached 21.7% and 2.8%, respectively, and the objective evaluation of the image quality was good. However, overall MOS was less than that of the single reconstruction, the perceptual quality was weakened, and the time cost was more than 130 times greater. The reconstruction effect of SRResNet was better than that of SRGAN. When SRGAN and SRResNet were used for the recognition of endocytic vesicles in Solanum nigrum root cells, the clarity of the reconstructed images was obviously improved, the boundary of the endocytic vesicles was clearer, and the number of identified endocytic vesicles increased from 6 to 9 and 10, respectively, and the mean fluorescence intensity was enhanced by 14.4% and 7.8%, respectively. Relevant research and achievements are of great significance for promoting the application of deep learning methods and image super-resolution reconstruction technology in laser confocal image studies.It is difficult for laser scanning confocal microscopy to obtain high- or ultra-high-resolution laser confocal images directly, which affects the deep mining and use of the embedded information in laser confocal images and forms a technical bottleneck in the in-depth exploration of the microscopic physiological and biochemical processes of plants. The super-resolution reconstruction model (SRGAN), which is based on a generative adversarial network and super-resolution reconstruction model (SRResNet), which is based on a residual network, was used to obtain single and secondary super-resolution reconstruction images of laser confocal images of the root cells of the hyperaccumulator  SRIR has great importance and value in practical applications such as medical imaging, satellite remote sensing, monitoring, and scientific research . TraditiSolanum nigrum. We compared super-resolution image reconstruction using SRGAN and SRResNet, evaluating different image quality indicators. Our results promote the intersection of artificial intelligence and botany, environmental science, ecology, and other disciplines and provide a new concept for the in-depth study of phytoremediation-related cytological characteristics.The potential to obtain high-definition images through image super-resolution reconstruction technology has attracted the attention of many researchers , but no 22.12.1.1SRGAN has the same overall structure as GAN. It is composed of a generative network and a discriminant network, and the perceived loss is used as the loss function . The gen2.1.2SRResNet is based on a convolutional neural network added to a residual learning network structure . The mai2.2In the laser confocal images reconstructed by SRGAN and SRResNet, the image quality was evaluated using three indices: peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and mean opinion score (MOS). The former two indicators are objective evaluations, based on experimental numerical calculations, and the latter is a subjective evaluation based on the system of visual perception of the human eye. Taking into account the efficiency factor of practical use, the time cost of the super-resolution reconstruction of laser confocal images using different models was also considered in this evaluation.2.2.1MSE of the image  in 1/2 Hoagland nutrient solution. About 1 cm fresh Solanum nigrum root was cut, and than in 0.5 mL of 10 μM FM4-64 dye (4-{6-[4-(Diethylamino)phenyl]-1,3,5-hexatrien-1-yl}-1-[3-(triethylammonio)propyl]pyridinium dibromide) was stained for 30 min. Then the samples were carefully removed and washed with deionized water to clean the surface of the dye, placed on slides, and sealed with coverslips. They were observed within 1 h via CLSM  using a complementary metal-oxide-semiconductor image sensor with an image resolution of 1024×1024. The light source is a laser with an excitation wavelength of 488 nm and an emission wavelength of 500—530 nm. After setting the parameters, use the 4× objective lens was used to find the sample under the bright field and adjust the position and lens focal length to bring the image to the middle of the field of view. Then, the 20× objective lens was applied, and the appropriate brightness of the field of view was adjusted. After this, the 60× oil lens was used, and mirror oil was dripped onto the surface of the objective lens. Next, the brightness of the field of view and the lens focal length were adjusted to clarify the image; finally, the bright field was closed, the laser was turned on, and the appropriate laser intensity was chosen to take the fluorescent image of the root hair cells.The test samples were collected from 2.3.2A dataset was produced using test samples from the confocal laser scanning microscope. The 8294 images that were obtained from sampling were used to create a dataset of which the ratio of the training set to the test set was 100:45, which meets the requirements of deep learning for the training and test sets without interfering with either. We selected 200 of these images as the set for training the model in each batch. Then, 90 were used as the test set to evaluate the model reconstruction effect.2.3.3Both models were implemented using the PyTorch framework. The training and testing processes were performed on the training and test sets, respectively, using Ubuntu on an Intel E5-2678 v3 64 G 1 TB + 256 G solid-state computer that had a dual NVIDIA 2080Ti (video memory 12 G).-4, and the backpropagation algorithm adopted was Adam  as single reconstruction and 16× image reconstruction  as secondary reconstruction were used here.-4 and the Adam back-propagation algorithm, 200 images were randomly selected from the training set in each batch, and the training images were input into the SRGAN and SRResNet models implemented using PyTorch. After 200 training batches were completed, effective weight parameters were obtained when the loss function was 0.0164.Using a learning rate of 2×103.1In all, 90 laser confocal images of the test set were input into the trained SRGAN and SRResNet models. For the same reconstruction times, the PSNR and SSIM values were higher in SRResNet than in SRGAN, the MOS values were lower in SRResNet than in SRAGN, and the time costs were similar between the two. This indicates that SRResNet had superior performance in improving the signal-to-noise ratio and image contrast, but had inferior perceived quality to that of SRGAN.3.2Solanum nigrum root cells containing endocytic vesicles and the 10× local magnification maps of endocytic vesicles. The image qualities of the SRGAN and SRResNet reconstructions were better than that of the original image. Further, the boundary clarities of endocytic vesicles were significantly better than those in the original image.Next, the reconstructed images were automatically identified using YOLOv5 for endocytic vesicles using the same model parameters and weights. Six, nine, and ten endocytic vesicles were identified in the three images, respectively. These results indicate that the reconstruction effectively improved the quantitative recognition effect, confirming the improvement of image quality. The image details were richer, which would aid the mining of the deep-level information contained in the image. The recognition effect of the SRResNet-reconstructed image was better than that of the SRGAN-reconstructed image.Finally, the mean fluorescence intensity of each image shown in 4Solanum nigrum root cells. They also revealed more vesicles . Finally, the mean fluorescence intensity was enhanced by 14.4% and 7.8%, which indicates the feasibility of deep mining of the embedded information in laser confocal images.Super-resolution reconstruction technology based on deep learning can be used in the study of laser confocal images. Single reconstruction (4×) and secondary reconstruction (16×) using the SRGAN and SRResNet models significantly improved the evaluation indicators PSNR, SSIM, and MOS of the image as well as the resolution. The maximum PSNR was 47.690, indicating that the quality of reconstructed image was significantly improved. The PSNR and SSIM of the secondary reconstruction image were obviously better than those of the single reconstruction, with maximum increases of 21.7% and 2.8%, respectively. Additionally, the objective evaluation of the image quality was good, but the MOS was generally lower than that of the single reconstruction, and the perceptual quality was weakened. The reconstruction effect using SRResNet was better than that obtained using SRGAN. The images reconstructed by SRGAN and SRResNet also clarified the boundaries of endocytic vesicles in The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.Solanum nigrum endocytosis vesicles. All authors contributed to the article and approved the submitted version.FH and FW conceived and designed the research, and guided the preparation and writing of the manuscript. WL searched for relevant literature, carried out super-resolution reconstruction of images based on laser confocal images, and wrote the manuscript. DH and YL sampled and collected those laser confocal images of hyperaccumulator


SSIM). In response to the traditional BM3D algorithm that directly performs block-matching on a noisy image, without considering the deep-level features of the image, we propose a method that performs block-matching on the feature maps of the noisy image. In this method, we perform block-matching on multiple depth feature maps of a noisy image, and then determine the positions of the corresponding similar blocks in the noisy image based on the block-matching results, to obtain the set of similar blocks that take into account the deep-level features of the noisy image. In addition, we improve the similarity measure criterion for block-matching based on the Structural Similarity Index, which takes into account the pixel-by-pixel value differences in the image blocks while fully considering the structure, brightness, and contrast information of the image blocks. To verify the effectiveness of the proposed method, we conduct extensive comparative experiments. The experimental results demonstrate that the proposed method not only effectively enhances the denoising performance of the image, but also preserves the detailed features of the image and improves the visual quality of the denoised image.We propose an improved BM3D algorithm for block-matching based on UNet denoising network feature maps and structural similarity ( In the process of image generation and transformation, noise is inevitably generated, which not only reduces the visual quality of the image but also affects subsequent advanced visual processing tasks . TherefoAt present, image-processing algorithms can be roughly divided into three categories: spatial-domain denoising, frequency-domain denoising ,3, and dAccording to the BM3D step, block-matching is a critical procedure of the algorithm. Currently, there has been a lot of research on block-matching in the academic community. Refs. ,21,22 haIn recent years, the rapid development of deep learning has provided ideas for the extraction of deep features in images. Deep learning has the advantages of autonomous learning of target features and a high recognition rate ,27. The SSIM [SSIM to improve the traditional similarity measurement and propose a new similarity measurement criterion that considers the structural similarity of noisy images. The contributions of this paper are three-fold:In addition, the similarity measurement for block-matching in the traditional BM3D algorithm is based on pixels to calculate the similarity between two images, which is too simple and direct to consider the depth features of the noisy image. The SSIM  is an in(1) A pre-trained UNet denoising network is utilized to obtain feature maps at different scales of the noisy image, and then block-matching operations of the BM3D algorithm are performed on the feature maps. The block-matching results are mapped to the corresponding positions of the noisy image, therefore obtaining block-matching groups that take into account the depth characteristics of the noisy image.SSIM has the characteristics of image structure, brightness, and contrast, an improved similarity measurement criterion based on SSIM is proposed.(2) Considering that (3) Based on classic image data, extensive comparative experiments and depth analysis are conducted to explain the value of considering image depth features for block-matching operations proposed in this paper.SSIM. The The other parts of this paper are organized as follows. The The BM3D algorithm is mainly divided into two steps. The first step is the basic estimation, and the second step is the final estimation. The first and second steps both include block-matching grouping, filtering, and aggregation. The execution process of the algorithm is shown in p on the noisy image I. Set the current block being operated on as Construct a two-dimensional sliding window with a size of The idea of finding similar blocks for reference block Perform a three-dimensional unitary transformation After hard threshold shrinkage, each block will obtain a basic estimated weight value, Equation .(3)ωRbai that may appear in multiple blocks, it is necessary to perform a weighted average on the estimated values of these overlapping blocks to obtain the basic estimation value of i. The formula is as follows:i in the set i. Therefore, it is necessary to weighted-average the estimated values of all blocks containing that pixel in the set. i in any set For a pixel Based on the basic estimation image Unlike the use of hard threshold filtering in the basic estimation stage, the final estimation utilizes Wiener filtering. The attenuation co-efficient of the Wiener filter is calculated as follows:Then, the Wiener filtering of The weight of each reference block in the final estimation stage can be defined as:i. The formula is as follows:i in any set Similarly, it is necessary to perform a weighted average on these block estimates to obtain the final estimate of the pixel Equation .SSIM. The specific instructions are as follows.The process of searching for similar blocks in the traditional BM3D algorithm is carried out on the noisy image, which does not take into account the deep-level features of the noisy image and may lead to inaccurate block-matching results. To fully consider the depth features at different scales of the noisy image and improve the accuracy of block-matching, we propose the modified block-matching based on noisy image depth features and the improved similarity measurement based on Given the advantages of deep-learning networks in extracting deep-level features of images, we select a pre-trained UNet denoising network to extract feature maps of the noisy image, and then search for similar blocks on the feature maps. Based on the results of block-matching on each feature map, all corresponding similar blocks are searched for at the same position in the noisy image, thus obtaining a set of similar blocks that consider the deep-level features of the noisy image. Based on this, subsequent operations on BM3D are carried out. The execution process of the improved BM3D algorithm is shown in Compared with the flowchart of the traditional BM3D algorithm in Unlike The next step is to input the 3D matrix formed by the set of similar blocks of each block into the next step, which is the same process as the traditional BM3D algorithm. Similarly, after the first step of algorithm execution, the basic estimated image needs to undergo feature map extraction and modified block-matching. Similar block sets that consider deep-level features are obtained from the basic estimated image and merged to form a 3D matrix SSIM is a measure of image similarity, which effectively integrates the structure, brightness, and contrast information of the image. These features can promote human visual perception and have the advantage of considering image structural features. The expression of SSIM is shown in Equation SSIMSSIM. This has a good promoting effect on improving the visual quality of the denoised image.In Equation , the smaEquation , the disTo verify the denoising performance of the improved BM3D method proposed in this paper, we conduct extensive comparative experiments, and the experimental results are presented and discussed. First, based on the combination of different layer feature maps using the UNet denoising network to improve the denoising effect of BM3D, the optimal feature map combination is selected. Second, compare the improved BM3D method proposed in this paper with other classic denoising algorithms and display the denoising effect diagram. Then, compare the denoising performance of various denoising algorithms on images with different noise levels. Finally, the contributions of different components in the proposed method are analyzed.PSNR) and SSIM as evaluation indicators for image-denoising performance. The larger the PSNR, the better the denoising effect. The larger the SSIM, the higher the visual quality of the denoised image. The parameter settings for traditional BM3D and improved BM3D algorithm are the same, as shown in In this experiment, we utilize classic test images for image denoising, such as Cameraman, boat, and Baboon. Each image is a Grayscale with a size of 256 × 256, including both noiseless images and images with various noise levels. We adopt pre-trained UNet network models on datasets with different noise levels and utilize these UNet models to extract feature maps of images with different noise levels for experiments. In addition, we utilize the peak signal-to-noise ratio , down_Path.4.block.1 (feature layer 2), up_Path.4.conv_2.Block.1 (feature layer 3) and last.2 (feature layer 4). Due to the fact that each layer has multiple channels, there are many combinations of feature maps, including feature map combinations of different channels in any of the above four layers. The main purpose of this paper is to verify that the denoising performance of the improved BM3D algorithm based on deep feature maps is superior to the traditional BM3D algorithm. Therefore, for the convenience of the experiment, the way of obtaining feature map combinations in this experiment is as follows: first, select any two layers from the above four layers, with a total of 10 combinations, including 1&1 (representing feature layer 1 and feature layer 1), 1&2 , 1&3, 1&4, 2&2, 2&3, 2&4, 3&3, 3&4, and 4&4. Then select one channel from each of these two layers and treat the selected two channels as a combination, without considering other combinations. Based on the principle of feature map combination selection above, we calculate the PSNR appear in the combination of layers 4&4. In addition, the denoising effect of the combination containing feature layer 2 is not good, especially the denoising effect of the 2&2 combination is the worst in each image. Further observation reveals that the PSNR values of various combinations exhibit a pattern of 1&2 < 1&3 < 1&4, 2&2 < 2&3 < 2&4, indicating that the denoising effect of the feature map of the later layer is better than that of the front layer. This is because the structure of the UNet denoising network is similar to the Autoencoder, and feature layer 2 is located in the down-compression process, so the feature map of this layer will become fuzzy, and the features are not obvious. Therefore, the effect of block-matching with the matching of feature layer 2 is not good. Feature layer 3 is located in the up-decoding process. After denoising by the network, the feature map of this layer begins to become clear, and the features are obvious. Feature layer 4 is located in the last layer of the last process, which is the output of the UNet network. Therefore, the feature map image of this layer is the clearest and the features are also the most obvious. Based on the above experimental results and analysis, the improved BM3D algorithm in the subsequent experiments in this paper adopts a combination of feature maps of layers 4&4.From To evaluate the performance of the improved BM3D algorithm proposed in this paper, we compared two classic nonlocal mean denoising algorithms, BM3D and NLM, respectively. We also selected three advanced deep-learning denoising networks, namely BM3D-Net , DIVA 1 and UNetPSNR and SSIM values than the other four methods. Compared to UNet, our method has an average increase in PSNR of 3.24 dB, and an average increase in SSIM of 0.2181 compared to NLM. Compared with BM3D and NCSR, our method has an average increase of 0.26–0.39 dB in PSNR value and 0.0076–0.011 in SSIM value. Compared with advanced DIVA and BM3D-Net, our method has some advantages in PSNR. In addition, for individual images, DIVA’s SSIM is better than our method, but overall, our method has better denoising performance. To further demonstrate that our method outperforms the other methods in terms of denoising performance, we utilize the image of Baboon as an example to visualize the denoising effects of various methods with different noise levels. In SSIM to improve the similarity measurement criterion for block-matching, which further improves the visual quality of the denoised image.PSNR values are visualized in PSNR value of the BM3D-Net algorithm at a noise level of PSNR value at a noise level of From We conduct ablation experiments to better analyze the contributions of different components of the method proposed in this paper. In the following example, we test four methods with different components.(1) BM3D: Traditional BM3D method.SSIM: A method for improving traditional BM3D based on SSIM.(2) BM3D + (3) BM3D + Feature: A method for improving traditional BM3D based on UNet de-noising network feature maps.SSIM + Feature: A method for improving traditional BM3D based on SSIM and UNet denoising network feature maps.(4) BM3D + SSIM is better than that of the traditional BM3D method, which is reflected in the average increase of 0.06 dB in PSNR value and 0.0019 in SSIM value. This is because the SSIM considers the structure, brightness, and contrast information of the image, so the denoised image has higher visual quality. Second, the denoising effect of the improved BM3D based on UNet denoising network feature maps is superior to the traditional BM3D method, specifically reflected in an average increase of 0.19 dB in PSNR value and 0.0055 in SSIM value. This is because block-matching on feature maps takes into account the depth features of different scales of the noisy image, resulting in more accurate block-matching results and better denoising effects. Third, the denoising effect of the improved BM3D based on SSIM and UNet denoising network feature maps is higher than the other three methods. Compared with the traditional BM3D algorithm, the average PSNR value increases by 0.26 dB, and the average SSIM value increases by 0.0076. The above experimental results verify that both the UNet denoising network feature map and the improved BM3D algorithm based on SSIM proposed in this paper can improve the denoising performance of the traditional BM3D algorithm.Based on the experimental results in To verify the denoising potential of the denoising method proposed in this paper, we selected some real-life image data for denoising, including one satellite image, one map image, one ball image, and one calligraphy image. As shown in PSNR and SSIM numerical results of the noisy and denoised images at the bottom of the image. On the one hand, it can be seen from the denoised image and the enlarged image of local details that the proposed method effectively removes noise from the image and preserves the detailed features of the image clearly. On the other hand, from the numerical results, it can be seen that compared with the PSNR and SSIM values of noisy images, the proposed method has significantly improved PSNR and SSIM. From the above analysis, we can conclude that the improved BM3D method proposed in this paper also has excellent denoising effects on real-life images and has good denoising potential.We add Gaussian noise with a noise level of SSIM). Compared to the traditional BM3D algorithm that directly performs block-matching on the noisy image, this method takes into account the deep-level features of different scales of the image and improves the accuracy of block-matching. Moreover, SSIM is utilized to improve the similarity measurement criterion, taking into account the structure, brightness, and contrast information of the image, further enhancing the denoising effect of the BM3D algorithm. In addition, it should be pointed out that the time complexity of the method proposed is relatively high, mainly due to the use of the SSIM to improve the similarity measurement criterion for BM3D block-matching, which involves SSIM calculation between many blocks of the image. In areas with high requirements for image quality, the proposed method can play a good role in non-real-time applications such as image restoration and image enhancement. Finally, through several experiments, the following conclusions can be drawn:We propose an improved BM3D denoising algorithm based on the UNet denoising network feature map and Structural Similarity Index ((1) Feature map combinations from different layers of the UNet denoising network are utilized to improve the block-matching process of BM3D, the denoising effect of feature map combinations containing feature layer 2 is poor, while the denoising effect of feature map combinations containing feature layer 4 is excellent.PSNR and SSIM. In addition, the proposed method outperforms other methods in denoising images with different noise levels.(2) Compared to the six classic denoising algorithms of traditional BM3D, NLM, UNet, NCSR, DIVA, and BM3D-Net, the improved BM3D method proposed in this paper both has advantages in (3) The block-matching process of the improved BM3D algorithm proposed in this paper fully considers the deep-level features of the image at different scales, as well as the structure, brightness, and contrast information of the image. After denoising, the detailed features of the image can be well preserved.SSIM in the proposed method in this paper both have a promoting effect on improving the denoising performance of the traditional BM3D algorithm. Using both to improve the denoising effect of the traditional BM3D algorithm is better than using either of them alone.(4) The UNet denoising network feature map and


In this paper, we consider natural and modified polysaccharides for use as active ingredients in scale deposition inhibitors to prevent the formation of scale in oil production equipment, heat exchange equipment, and water supply systems. Modified and functionalized polysaccharides with a strong ability to inhibit the formation of deposits of typical scale, such as carbonates and sulfates of alkaline earth elements found in technological processes, are described. This review discusses the mechanisms of the inhibition of crystallization using polysaccharides, and the various methodological aspects of evaluating their effectiveness are considered. This review also provides information on the technological application of scale deposition inhibitors based on polysaccharides. Special attention is paid to the environmental aspect of the use of polysaccharides in industry as scale deposition inhibitors. Currently, the formation of deposits of poorly soluble salts from natural and technical aqueous solutions is a fundamental problem ,2,3,4. NTo date, various types of mineral deposits have been studied , and sev3, CaSO4, Fe(II), and Zn(II) sulfides 3+ and [Pb(H2O)6]2+, and in the form of soluble silicon complexes. It is also necessary to take into account the numerous associative compounds between Mg2+ and SO42− ions and unstable carbonate complexes, which decompose with decreases in pressure and oxygen access. In the presence of CO2 in systems with high salinity, well-known patterns of salt solubility are not applicable and, therefore, often lead to misinterpretation and incorrect estimates regarding precipitation and the sediments that arise during production processes. The following ion concentrations are present in natural water /[Ca2+], it was possible to achieve the formation of microspheres [The crystalline form of calcium carbonate in scale deposits is mainly represented by calcite, aragonite, and, to a lesser extent, vaterite ,60,61, ae formed . It is oopes. In , the ratshown in ,65. It wshown in . At t = 3 crystallization was investigated in [42− could stabilize the resulting vaterite, mainly formed during the first minutes of crystallization, at a ratio of SO42−/CO32− < 1 after 24 h. Calcite is a single phase in the mixture, but at a ratio of SO42−/CO32− > 1, vaterite can persist for a long time 3O−, that is, the precursor of silica deposition position . Poly(1-Cationic inulin (CATIN) with varying degrees of substitution (the degree of substitution (DS) is defined as the average number of cationic groups per monomer) stabilizes colloidal silica particles, preventing the formation of polymer silica molecules , and the3 deposition [2+ ion and participate in the formation of hydrogels and in the subsequent formation of calcium carbonate deposits on the cell walls of brown algae. The predominance of polysaccharides in biominerals is an incentive for in vitro research to study their effect on the origin and growth of inorganic phases, as well as to develop methods for creating new hybrid materials, often using calcium carbonate as a model system based on an understandable mechanism. Moreover, research in the field of biomineralization is also of practical importance, resulting in new materials for controlling undesirable scale deposition processes in technological systems. Of course, an important stage was the study of the mechanism of crystallization in the presence of polysaccharides. The works [The mechanism of inhibition of scale formation through polysaccharides for deposits commonly found in technological processes is hardly fundamentally different from inhibition through polymer compounds, such as polycarboxylates, polysulfonates, or any other organic additives . The anaposition , and parposition . Alginatposition , can forhe works ,75,119 j3, polysaccharides were used as model additives . Nonionized polymers, such as arabinogalactan and agar, could also stabilize the ion cluster, while Pec Es, on the contrary, destabilized the ionic associates in solution. The mechanism of destabilization is not related only to the polymer charge, but it is also necessary to take into account such properties as the chain length and the conformation and branching of the polymer. The crystallization-inhibiting properties are also determined through the time of the formation of ion clusters, so Arb and Dex S practically did not affect the time of the cluster formation, but all other polymers increased the time from 1.16 to 1.66, with Hep reaching 4.35 times. It is assumed that polysaccharides prevent the formation of embryos by stabilizing the colloidal structure of clusters preceding the formation of embryos [2+ ion; however, the nature of complexation can be different. For example, Pec Es and a number of other polysaccharides are able to form complexes with a single polymer chain through the electrostatic and donor–acceptor association of poly-D-galacturonate residues, and by crosslinking two parallel chains along the “egg-box” type [3 [To assess the effect on the crystallization of CaCOdditives , includi embryos ,121. An ox” type . It is o type [3   and polyaspartate (PA) were considered as reagents in the process of injecting an inhibitor into the reservoir  instead 3 crystallization through the use of carboxyinulin was carried out in [Kaff = kads/kdes is the equilibrium constant that is established between the crystalline phase and the inhibitor in the solution, characterizing the affinity of the inhibitor to the crystal surface; kads is the rate constant of inhibitor adsorption; kdes is the rate constant of the desorption rate; R0 and Ri are the rate of growth of the crystalline phase in the absence and in the presence of the inhibitor, respectively; and b is the factor limiting the effectiveness of the inhibitor.A detailed study of the mechanism of the inhibition of CaCOd out in . Three sSome of the results obtained in this work are presented in Kaff × 107 determined from Equation (7) was equal to 6.68, 7.94, and 32 L/mol for CMI-15, CMI-20, and CMI-25, respectively. Phosphonate inhibitors, such as oxyethylene diphosphonate and ethylenediaminetetrakismethylene phosphonate, had slightly lower values of 3.7 × 106 and 1 × 107, respectively.The constant 3 crystals. The results of this study indicated that CMI interacted with Ca2+ ions on the surface of the forming crystal due to carboxyl groups and due to the size of the macromolecule, making the surface inaccessible for crystal growth. These results were in good agreement with the results of molecular dynamics modeling [The higher inhibition efficiency of CMI-25 compared with CMI-15 and CMI-20 was associated with a higher affinity for CaCOmodeling .Among polysaccharides, active carboxyl groups include, for example, carboxymethyl cellulose and its sodium salt. The availability, environmental friendliness, thermal stability, and biodegradability of Na-carboxymethyl cellulose justify the development of new directions for its application ,129.2+ ion, which can reduce the precipitation of calcium salts from supersaturated aqueous solutions and prevent the formation of insoluble salts [Na-carboxymethylcellulose is one of the most accessible water-soluble polysaccharides, in which the carboxymethyl group is bound to the hydroxyl groups of β-(1→4)-D-glucose monomers ,129. NaCle salts .3 and CaSO4 crystals showed that in the presence of NaCMC, there was a decrease in the average crystal size by ~7.4 μm for CaCO3 and by 17.2 μm for CaSO4, as well as displaying the formation of a narrower interval of their distribution, from 1.50 to 7.52 microns for CaCO3 and from 1.15 to 11.8 μm for CaSO4  and soluble starch on the precipitation of calcium carbonate in the model system was discussed in detail in another article . In the vaterite . The effvaterite .The revealed general principles and patterns allow us to propose solutions claimed in patents ,136. As 3 crystallization processes. The properties of chitosan derivatives as compounds affecting the crystallization of calcium carbonate were studied in [6 was reduced to the modification of CaCO3 crystals due to complexation with Ca2+ ions in solution and on the crystal surface. The modified chitosan oligomer [3 crystallization-inhibiting properties, showing, under comparable conditions, a higher efficacy than other known inhibitors, such as, for example, polyaspartate.Polysaccharides of animal origin, such as chitin, are important biomacromolecules of invertebrates that can influence the processes of calcium carbonate biomineralization. Chitin is poorly soluble in aqueous solutions in this regard; hence, water-soluble chitosan and its derivatives are used to study its effect on CaCOudied in ,137. It udied in  with a mIn recent decades, there have been intense developments in the research field of the inhibition of the crystallization of carbonate, sulfate, phosphate scale, and silicates, as well as improvements in their technological applications for managing scale deposition in industrial water supply processes for use in oil production. In the systematic search for new inhibitors, the study of crystallization processes in their presence and the substantiation of inhibition mechanisms makes it possible to create effective reagents, develop new schemes and application technologies, and determine the limits of the possible effect of inhibitors on scale deposition in production processes. Of particular note is the trend witnessed in the last two decades associated with the development of inhibitors that do not create an additional burden on the existing ecological balance. From a technological point of view, it is important to move towards the development of complex reagents that allow for several problems related to managing complications to be solved at once, for example, for application in oil production. This approach not only makes it possible to solve technological problems, but also opens up opportunities to reduce the use of chemical reagents.