The reversible shrink–swell behavior of expansive soil imposes a serious challenge that threatens the overlying structures’ safety and durability. Traditional chemical additives such as lime and cement still exhibit satisfying performance over their counterparts in terms of swelling potential reduction. Nevertheless, significant concerns are associated with these chemicals, in addition to their environmental impact. This paper proposes a novel application of the closed-cell one-component hydrophobic polyurethane foam (HPUF) to stabilize the swelling soil. An extensive experimental study was performed to assess the efficiency of HPUF in mitigating both the swelling and shrinkage response of high montmorillonite content expansive soil. Expansive soil was injected/mixed with different weight ratios of the proposed stabilizer, and the optimum mixing design and injection percentage of the foam resin were identified to be ranged from 10% to 15%. The shrink–swell behaviors of both injected and noninjected samples were compared. Results of this comparison confirmed that HPUF could competently reduce both the swelling potential and the shrinkage cracking of the reactive expansive soil, even after several wet-shrink cycles.  Despite the carbonation attacks, inefficiencies in controlling shrink–swell behavior of high sulfate expansive soils and their environmental have an impact on the greenhouse gas production salinization of water resources. Several case studies available in the literature ,3,4,5,6 Recently, new additives were adopted to treat the problematic swelling soil, such as the pozzolanic and recycled materials of fly ash, silica fume, etc. Several studies assessed the effectiveness of these new additives by comparing their effect with the effect of traditional solutions (lime or cement) in terms of the swelling pressure and potential. Among the different categories of additives employed for stabilizing expansive soils, the traditional chemical additives (lime and cement) still exhibit better performance over the other treatments, as concluded by Reddy et al. . DespiteThe current experimental study aspires to introduce a novel application for the closed-cell hydrophobic polyurethane foam (HPUF) in mitigating both the swelling and shrinkage response of the high montmorillonite content expansive soil. A comprehensive experimental study is performed to assess the efficiency of HPUF in stabilizing the swelling soil. Different weight ratios of the polymeric stabilizing agents are either injected into or mixed with the bentonite samples. The behavior of the expansive soil with and without the injected foam are compared to evaluate the capability of HPUF in reducing the swelling potential of expansive soil and to identify the optimum injection ratio.Many studies discussed the swell–shrink mechanism of expansive soil ,14,15,16With that in mind, the injection of energy-absorbing material such as polyurethane foam into the swelling soil is expected to compress it horizontally and/or vertically. Consequently, this reduces the vertical soil heave by absorbing a large amount of the soil energy once the PU foam is compressed in the horizontal direction, as shown in The mineralogical, physical, and chemical properties of the selected high montmorillonite content expansive soil and the mechanical properties of the hydrophobic closed-cell polyurethane foam (HPUF) are also identified and discussed in this section.The degree of expansion depends upon the type and amount of clay minerals and exchangeable ions . Two priBentonites are high-swelling clays with low hydraulic conductivity (k) that are commonly used in a wide range of geotechnical and geoenvironmental applications. With that in mind, because of its high montmorillonite content, the pure high swelling sodium-based bentonite (Na-bentonite) provided by Nano Technologies Co.  was specially selected for this experimental study. The product manufacturer provided the specific gravity and dry density, and some other Na-bentonite index properties were obtained from different soil laboratory tests that were performed through this study. The physical and chemical properties and the mineralogical composition of the Na-bentonite are summarized in Polyurethane foam materials are classified as polymer materials. They are commonly used as protection from impacts and to absorb energy . PolyureThe use of expanding polyurethane as a filling and lifting agent in soft soils was reported as an effective stabilizer in many cases, specifically for treating problems associated with excessive and differential settlements. The high expansion characteristics of the polyurethane foam volume were beneficial in maintaining deformed pavement, buildings, and other underground construction, where traditional or conventional techniques were infeasible ,32. Limis), shrinkage cracking, and swelling soil response after several wet-shrink cycles were determined utilizing the Oedometer test [An extensive experimental study was performed to assess the efficiency of the closed-cell hydrophobic polyurethane foam (HPUF) in mitigating both the swelling and shrinkage response of the high montmorillonite content Na-bentonite clay. For this purpose, the experimental study included three main parts. First, the swelling–shrinkage behavior of the Na-Bentonite clay was examined. The swelling potential , to be computed with time.One-dimensional swelling tests were carried out following ASTM D4546-96  method b. A chamOn the other side, the induced cracks due to the expansive soil’s shrinkage were explored. Bentonite samples were prepared with a similar approach and placed in 100 mm plastic transparent plates . The losIn addition, eight bentonite samples with a 100 mm diameter and 300 mL volume were prepared and tested in three wet-shrink cycles through a large-scale free swell arrangement to assess the change in swelling behavior after different cycles . In eachIn this study, the one-component, closed-cell hydrophobic polyurethane foam (HPUF) was utilized. For this PU foam class, water is the only blowing agent that affects the formation of the hardened foam; 10% is the recommended catalyst ratio to be added to the polymer resin in order to optimize the chemical reaction time. However, no recommendation was provided by the manufacturer for the optimal water content. Therefore, four different mixes were conducted to investigate the effect of exothermic reaction between the foam resin and the water content on the microstructure, density, and mechanical properties of the produced semirigid PU foam material. Water contents adopted for these four mixes were 10%, 20%, 50%, and 100%. In addition, 30 mL and 3 mL were the volumes utilized for the foam resin and catalyst, respectively, in the four mixes. One fundamental point to note is that all mixes were prepared at the same temperature (20 °C). The foam expansion ratio, hardening time, and density were obtained for the four mixes with different water contents. This experiment was repeated three times, and the error percentages were determined.To investigate the effect of the foam density on the mechanical behavior of the HPUF, four samples were prepared with the same percentages addressed above to be tested under compression stress. The four samples were cast in a custom cylindrical stainless-steel mold with an extruder. According to ASTM D1621-16 , sharp cNa-bentonite samples were prepared with the similar approach explained in This section discusses the efficiency of the HPUF in mitigating both the swelling and shrinkage response of the high montmorillonite content expansive soil. The HPUF’s efficiency was assessed by comparing the behavior of the injected/mixing soil samples with the original Na-bentonite samples’ behavior in terms of the swelling potential, expansion rate with time, induced shrinkage cracking, and swelling response after three full wet-shrink cycles.3 when the optimal water content was considered as 15%. One fundamental point to note is that the obtained optimal moisture content was adopted for preparing all bentonite samples examined within this study.The maximum dry unit weight and the optimal moisture content of the Na-bentonite were determined through the compaction test. As shown in Results of the one-dimensional swelling test performed on the two Na-bentonite samples indicated that bentonite samples started to swell and increase in volume once they were submerged. As shown in On the other side, the loss in specimen volume and the cracking propagation were monitored for 120 h. The first expectation for using the PU foam as a stabilizer for expansive soil was that it would act as a compressible material with high energy absorption capacity and reduce the soil’s swelling potential. Existence of this compressible media may reduce the restraints in the lateral direction to decrease the expected swelling in both vertical and lateral directions. This concept is close to expanded polystyrene (EPS) geofoam’s mechanism discussed by Ikizler et al. . HoweverIt also can be seen from The four HPUF samples were also tested under compression stress according to ASTM D1621-16 . Table 43, and the compressive stress at 10% strain of the PUFs increases from 0.055 to 0.098 MPa. However, the result of the first foam sample was inconsistent with those of the other three. Despite the high density of the first sample (HPUF 1), it required less compressive stress to achieve the 10% strain level than the second foam sample (HPUF 2). This can be explained by the lower expansion ratio of the first foam sample caused by the formation of relatively rigid walls around the foam cell. It can be seen from Toughness is the material’s resistance to fracture or ability to undergo deformation, and it represents the energy absorption capacity. It can be calculated from the area under the curve in the stress–strain curve. To explore the effect of HPUF on swelling behavior and obtain the optimal injection percentage, a series of swelling tests under constant stress (6.9 kPa) were conducted using an oedometer apparatus . ResultsThe effect of HPUF on reducing the induced cracks due to the shrinkage of the expansive soil was explored through four injected Na-bentonite specimens with 1%, 5%, 10%, and 15% HPUF weight ratios. The loss in specimen volume and the cracking propagation were monitored for 120 h, as presented in The efficiency of the HPUF in mitigating the swelling potential of the expansive soil after several wet-shrink cycles was assessed through a large-scale free swell arrangement that was used to test four bentonite samples. The first two bentonite specimens were noninjected , and the other two were mixed with 10% of the HPUF. In each cycle, the four bentonite specimens were submerged in 900 mL of distilled water, and swelling potential results were taken after 72 h. Around 21 days was the time required for water evaporation, and the samples were allowed to shrink for another 5 days. As shown in According to the experimental study , lime trAs a future research line, the paper reveals an approach that can be reutilized to evaluate the efficiency of the HPUF in mitigating the swelling stress of expansive soil. This will be beneficial to monitor and study the response of expansive soil with the HPUF stabilizer. In addition, experimental studies may extend this research by investigating the effect of the HPUF on soil bearing capacity and other soil’s compressibility properties.Field studies should be carried out to investigate the different injection and mixing methods and highlight the superior implementation technique that fits HPUF characteristics and leads to the maximum reduction in soil swelling. Moreover, the field study may also investigate the effective performance of the HPUF over time and under different destructive chemical, physical, and mechanical conditions. This can be done through different aging tests.Similar to EPS foam, the HPUF can be implemented as inclusions. However, numerical analyses should be performed to explore factors affecting the stabilized soil’s swell–shrink mechanics using this suggested stabilizer. This parametric study may include the effect of inclusion diameter and length, the spacing between the foam columns, and the effect of the permeability parameters for both the soil and foam material.HPUF could competently reduce both the swelling potential and the shrinkage cracking of the reactive expansive soil, even after several wet-shrink cycles.Single-component polyurethane foam’s mechanical properties, including density, reaction time, the expansion ratio, are affected by the water content (blowing agent) that is added for the chemical reaction during the formation process. The density of the HPUF increased with the increase in water content. The mechanical properties of the foam sample with 50% water content were superior to the other three samples in terms of energy absorption capacity, strength, and compressibility at both elastic and failure phases.Results of free swell tests showed that HPUF could successfully reduce the swelling potential of the expansive soil with an average reduction percentage of about 50%, and the optimal injection percentage can be ranged from 10% to 15%.The analyses involving the cracking patterns and the change of moisture content with time showed that HPUF significantly contributes to stabilizing the retained water for a longer time into the soil. Therefore, water evaporated more gradually from the samples, which pointedly reduced the shrinkage cracks in the specimens injected with relatively high weight ratios (10% and 15%).In particular, the HPUF stabilizer was able to mitigate the expansive soil’s swelling potential along three different wet-shrink cycles. This may be attributed to the polymer’s ability to generate a nanocomposite structure that gives the soil more resistance against the volume change, in addition to its ability to superglue the soil particles and produce a more stabilized system that can resist the volume change. Dissimilar to lime and fly ash, HPUF does not require curing, thus requiring shorter construction times. In addition, HPUF resin can be mixed or injected into the soil. These key merits empower the HPUF to be a comparable stabilizer that can be used to overcome the associated problems with the traditional chemical stabilization methods. This paper introduced a novel application of the closed-cell, one-component hydrophobic polyurethane foam (HPUF) to be used as a swelling soil stabilizer. Results of the laboratory tests performed confirmed that HPUF efficiently succeeded in mitigating both the swelling potential and the shrinkage cracking of the reactive expansive soil, even after several wet-shrink cycles. In addition, based on the results of the experimental study performed, the following points can be drawn:


This paper empirically models the dynamics of Brazilian government bond (BGB) yields based on monthly macroeconomic data, in the context of the evolution of the key macroeconomic variables in Brazil. The results show that the current short-term interest rate has a decisive influence on the long-term interest rate on BGBs, after controlling for various key macroeconomic variables, such as inflation and industrial production. These findings support John Maynard Keynes’s claim that the central bank’s actions influence the long-term interest rate on government bonds mainly through the current short-term interest rate. These findings have important policy implications for Brazil. This paper relates the findings of the estimated models to ongoing debates in fiscal and monetary policies. John Maynard Keynes in [1: 352–64] argued that a country’s central bank has a decisive influence on the long-term interest rate on government bonds mainly through its monetary policy. He believed that the central bank’s policy rate sets the current short-term interest rate, which in turn has a crucial effect on the long-term interest rate. This paper examines whether Keynes’s hypothesis that the current short-term interest rate is the key driver of the long-term interest rate holds for Brazil, after controlling for several key macroeconomic variables, such as inflation, and the pace of economic activity or industrial production.This paper contributes to the literature on the dynamics of government bond yields by examining Brazilian government bond (BGB) yields from a Keynesian perspective. Understanding the empirics of BGB yields is an important question, not just for macroeconomists but also for policymakers and domestic and international investors in Brazilian financial markets. The empirical findings pertaining to the dynamics of BGB yields can be useful for policy purposes. It can be useful for analyzing the effects of fiscal and monetary policy and the monetary transmission mechanism on financial markets. It is also germane for portfolio managers and investors interested in asset allocation in emerging markets and the public sector managers of government debt and Treasury operations. There are only a few empirical analyses of Latin American government bond yields from a Keynesian perspective. Hence, this paper fills a relevant gap in the literature. It provides some valuable insights about the relevance of the Keynesian perspective on government bond yields and financial markets to both macro theorists and policymakers.The paper is structured as follows. Section II discuss the relevant literature on government bond yields. It also summarizes the literature on the term structure of interest rates. It relates this paper to ongoing debates in the empirical literature on government bond yields. Section III gives a short summary of the evolution of BGB yields with reference to the relevant macroeconomic developments in Brazil. Section IV explains the data and relates the variables to the behavioral equations of the models. It presents the vector error correction method (VECM) applied in the paper. It reports and interprets the empirical findings from the models estimated here. Section V concludes with a discussion of the economic policy implications of the empirical findings for Brazil. There is a substantial literature on empirical models of government bond yields . ProvideThe dominant view is that a higher government debt or deficit ratio leads to higher government bond yields. This viewpoint is represented by the neoclassical perspective, such as –15. ThisThere are three dominant theories of the term structure of interest rates, namely (1) the pure expectation theory, (2) the market segmentation theory, and (3) the liquidity premium theory. The pure expectations theory maintains investors in bonds are generally indifferent to the maturity tenor of bonds and that bonds are perfect substitutes for one another. In the pure expectation theory of the term structure, the forward rate represents the expectation for future spot rate . Is a moAlthough the main theories of the term structure differ from one another, these theories are derived from the loanable funds theory. However, the scope of loanable funds market varies among the theories. There is a single loanable funds market for all types of bonds in the pure expectation theory. However, in the market segmentation theory, there are separate loanable funds markets for particular segments of the bond market. In liquidity premium theory there is an integrated loanable funds market, but bonds of different maturities are imperfect substitutes and hence the long-term interest rate does not merely reflect the market expectation of future spot rates but commands a term premium reflecting uncertainty about the future. There is a substantial empirical literature on the term structure of interest rates, such as –24. A moIn contrast to the dominant views of the term structure of interest rates, there is a minority view which maintains that the central bank’s action, particularly its policy rate, is the key driver of government bond yields. This viewpoint originates from Keynes , 27, whoThe Keynesian approach to interest rate is represented in , 29–45. This paper contributes to the literature in several propitious ways. First, it econometrically models government bond yields in Brazil, a major emerging market country. It is useful to examine whether Keynes’s conjecture holds for an emerging market country, such as Brazil. Second, it extends the research program of the Keynesian approach on modelling government bond yields to the case of Brazil. Third, it relates the developments in the BGB market to macroeconomic fundamentals and recent economic developments in Brazil. Fourth, it discerns the implications of the findings from the empirical modelling of the dynamics of the government bond market in Brazil for fiscal and monetary policies not only in Brazil but also for emerging market countries that issues government debt in their own currencies and exercises monetary sovereignty. This paper contributes to the ongoing debates on the empirical analysis of dynamics of government bond yields in the growing literature on government bond markets in emerging market countries. See , 60–62 fThe figures below show the evolution of the relevant macroeconomic variables related to government bond yields in Brazil from 2007 to 2018. The shaded areas in light grey in the figures are the periods of recession. Since 2014 the Brazilian economy has slowed down noticeably. In recent years, Brazil has suffered from political uncertainty, weakness in growth, elevated inflation, and a deprecation of the currency and volatility in the exchange rate for the currency –67, evenThe scatterplot in These scatterplots demonstrate some fascinating patterns. First, the yields of long-term BGBs and short-term securities, as measured by swap rates, are positively and tightly correlated. Second, the changes of the yields of long-term BGBs and short-term interest rates, as measured by swap rates, are also positively correlated, though less so than in the levels of the yields. Third, these correlations weaken somewhat as the maturity tenor of the long-term BGBs rise.The variable for the BCB policy rates is the Sistema Especial de Liquidação e de Custódia (SELIC) daily effective rate. The variable for the short-term interest rate is the 30-day swap rate. The variables for long-term interest rates are the yields of 2-year and 10-year BGBs. The variables for inflation rates are year-over-year percentage changes in the CPI and the GPI. The variables for economic activity are year over year changes in the measures of real gross domestic product (GDP) and the index of industrial production.Monthly time series data are used in this paper. Some daily time series data have been converted to monthly series. The time-series data for most of the variables are from January 2007 to December 2018, which amounts to 12 years of monthly data, consisting of more than 140 monthly observations.In this paper, the primary goals are to (1) test Keynes’s hypothesis concerning the relationship between the current short-term interest rate and the long-term interest rate, and (2) understand the short-run and the long-run relations among BGB yields, the short-term interest rate, and other variables. The vector error correction model (VECM) is used to test the hypothesis and estimate coefficients. The specification of the behavioral equations presented here are consistent with Keynesian models of government bonds yields, such as –39. ThesSeveral tasks are undertaken to validate the estimation process before undertaking estimation with the VECM. First, tests are conducted for unit roots, followed by check for cointegration among the variables. Second, the optimal lag-length for the equations is determined using an appropriate statistical technique. Third, the VECM model is applied based on the Johansen cointegration  with optThe results of unit root tests for the variables and their cointegrating behavior based on lag length selection are provided in VECMs are appropriate when variables are first difference stationary while nonstationary in their levels. The unit roots tests, provided in The next three tables explain the results of VECM model. The analysis is based on the long-run relationship, the short-run relationship, and the adjustment from short-run deviation to long-run equilibrium.Z = ′Where Z = ′Where From the cointegrating rank test  short-term swap rate leads to higher (lower) long-term BGB yields, particularly in the front end of the BGB yield curve. The BCB influences BGB yields primarily through the SELIC target rate, affecting the short-term swap rate. The findings models emphasize the role of the swap rate as a key determinant of BGB yields and the shape of the yield curve.If the SELIC target rate is the primary driver of BGB yields, there are consequential implications for macroeconomics, financial markets, and monetary policy. The findings of the paper suggest BCB’s actions appear to have a decisive effect on BGB yields. The BCB’s policy rate has a marked impact on BGB’s nominal yields through its influence on the swap rate. A higher (lower) short-term interest rate is associated with higher (lower) government bond yields. The BCB influences BGB yields by influencing the policy rate on short-term interest rates, such as the swap rates and the effective SELIC rates. Given its monetary sovereignty, the BCB appears to have the operational ability and flexibility to effectively influence BGBs’ yields on government debt in local currency as necessary, provided that a regime of floating exchange rate is maintained. However, besides the SELIC target rate, the BCB has other monetary policy tools at its disposal, such as calendar-based and information-conditional forward guidance, policy pronouncements, large-scale asset purchases, and yield curve control that can also affect BGB yields.Besides the influence of the policy rate of BGB yields, the effects of other monetary policy tools should be a future research topic. The influence of the fiscal deficit ratio or the government debt ratio on BGB yields should also be carefully examined, particularly to determine whether the claim of the loanable funds theory has any empirical support. The BCB and other central banks of emerging market countries need to also pay attention to the exchange rate of their currency because the exchange rate because currency depreciation (appreciation) can have marked inflationary (deflationary) effects and real effects on effective demand and economic activity, exports and imports, competitiveness, and the standard of living.The BCB’s policy rate decision is affected by the statutory mandates, inflationary pressures, inflation expectations, and overall economic and financial conditions and various economic and political constraints. Nevertheless, the findings support the view that the BCB’s monetary policy actions are an important driver of the long-term interest rate and the shape of the yield curve.The results presented in the paper show that the Keynesian perspective on government bond yields can be useful for modelling the dynamics of the BGB yields. The results generally support that Keynes’s [1: 352–353] contentions that (1) “the long-term rate of interest will respond to the wishes of a Currency Authority which will be exerting its direct influence … mainly on the short-term rate” and (2) “the influence of the short-term rate of interest on the long-term rate is much greater than anyone … would have expected” appear to hold for Brazil.The findings from the paper can inform policy issues and discussions in Brazil related to BGB yields and the yield curve, government debt management, fiscal sustainability, fiscal policy and the efficacy of monetary policy and monetary transmission mechanism. The findings can also have policy implications for other emerging market countries, particularly in Latin America, which often face economic circumstances and financial markets conditions and institutions similar to those in Brazil. Earlies studies of other emerging markets with currency sovereignty, such as India , 32, aliThe understanding of the observed behavior patterns of the BGBs yields and its dynamics as provided in this paper can be useful to both long standing debates and ongoing controversies in macroeconomic theory on a wide range of topics, such as the effects of monetary policy, quantitative easing, operational issues in central banking , 71, theS1 Dataset(XLSX)Click here for additional data file.S1 Appendix(DOCX)Click here for additional data file.S2 Appendix(DOCX)Click here for additional data file.


In the field of underwater vision, image matching between the main two sensors  has always been a challenging problem. The independent imaging mechanism of the two determines the modalities of the image, and the local features of the images under various modalities are significantly different, which makes the general matching method based on the optical image invalid. In order to make full use of underwater acoustic and optical images, and promote the development of multisensor information fusion (MSIF) technology, this letter proposes to apply an image attribute transfer algorithm and advanced local feature descriptor to solve the problem of underwater acousto-optic image matching. We utilize real and simulated underwater images for testing; experimental results show that our proposed method could effectively preprocess these multimodal images to obtain an accurate matching result, thus providing a new solution for the underwater multisensor image matching task. In recent years, many organizations have gradually begun to obtain resources such as oil and gas from the deep sea to meet the needs of human and industrial sustainable development. With the gradual deepening of the development of marine resources, deep-sea exploration activities will become more frequent. Current mainstream deep-submersibles, such as autonomous underwater vehicles (AUVs) and remotely operated vehicles (ROVs) are also equipped with more advanced acoustic and optical sensors. These sensors have played an outstanding role in seabed geomorphological mapping, target recognition and classification, biological research, resource exploration, environmental monitoring, and other fields ,3,4,5,6.A basic research work to promote acousto-optic fusion technology is to realize the local feature matching of underwater multimodal images. However, the traditional matching ideas based on extracting regional local features such as points, lines, and contours, as described by ,10,11, uFirst, use the image attribute transfer algorithm to eliminate the difference between the acoustic and optical images. Second, a more expressive local feature descriptor based on deep learning is introduced to improve the accuracy and robustness of matching.Additionally, in view of the difficulty and high cost of underwater acousto-optic data acquisition, in order to better meet the needs of experiments and further optimization, this paper adopts the method proposed in  to complThe main framework of the paper follows. The research inspiration of underwater acoustic and optical image matching is based on multimodal image feature matching. Image matching is a classic research topic in the field of computer vision. It is mainly divided into region and local feature matching. Region matching focuses on the correlation within a group of comparative image sets . Local fIn 2004, Fusiello and Murino  earlier High quality image acquisition is the foundation of acousto-optic fusion technology research. For this, extensive research on improving and restoring underwater optical images has been carried out ,19,20. IIn the field involving underwater sonar matching, Vandrish  comparedPrevious studies in sonar image matching mainly focus on image processing of a single sensor; sonar image matching studies mainly serve for image registration ,21. In rLiu  tried toHyesu Jang  proposedIn this article, we propose a new method for matching underwater acoustic images and optical images. Based on the advanced image attribute transfer algorithm , we intrFor image fusion tasks, the best spatial transformation must be found to make the points in the same spatial position in the two images correspond. In this section, we propose a new method for underwater acousto-optic image matching to further solve the problem of acquiring data in underwater engineering tasks.Based on the analogy of acoustic and optical image attributes, it combines the advantages of CNN depth feature extraction to realize image visual attribute conversion, and then eliminates the differences between the acousto-optic images;To match the generated target image and the original image in the acoustic domain and the optical domain, respectively, using the current advanced learned descriptor;The data aggregation method is used to display the calibrated matching correspondence on the original acoustic and optical images.The method has the following three aspects:The process of the proposed method is depicted in The visual attribute transfer method introduced in  has beenL, the feature map has the content structure of image L = 5), the next step is to iterate from the high level to the low level. Its application in the field of underwater acoustic image and optical image fusion is described as follows:As shown in Initially, the features of As shown in F(x) is a vector in position Before image reconstruction, it is necessary to establish a position mapping between feature maps, that is, feature alignment. This method mainly adopts nearest neighbor field (NNF) for feature alignment, in which the algorithm used to calculate the approximate NNF between two images is well applied in . In thisEquation (1) could easily obtain four variables by using the ideal assumption conditions at the coarsest layer, and then obtain the analogy mapping relationship between the acousto-optic images at layer The intention of image reconstruction is that the ideal latent image rated in .L − 1 is gradually
explored.After feature processing is completed, to reversely infer the lower layer from the coarsest layer of the image feature layers, until the first layer gets B as an example, based on the ideal assumptions of the coarsest layer (L. Formulas follow:As shown in A and By using Equation (2) for analysis, In view of the self-limitations of the underwater acousto-optic image proposed above, we tried to introduce the current advanced learned descriptor HardNet to solve this challenging matching task. The design of HardNet is inspired by the Lowe’s matching criterion for SIFT. Many experiments show that its performance in matching task is much better than traditional local feature descriptors and other learned descriptors . The samOne training batch contains two batches and n matching pairs, each of which could be represented by HardNet introduces a new loss for metric learning based on the CNN structure of L2-Net , and hasThe design of the acousto-optic matching model adopts an end-to-end idea. In the preprocessing stage, we use the VGG19 model with better generalization performance. The process principle of the underwater acoustic and optical image matching could be expressed as the following Algorithm 1.Algorithm 1. Underwater acoustic and optical image matching algorithm (UAOM)In order to verify the effectiveness of the method in this paper, we selected 6 groups of image pairs for the test experiment, as shown in Image pairs 1 and 2 are paver bricks and a hatch of one sunken plane, respectively, in which their acoustic images were captured by the ARIS Explorer 3000, which is dual frequency identification sonar (DIDSON); all the original images were provided by . .Image pair 3 is a mine shape and image pair 4 is a letter string; both their acoustic images were obtained by DIDSON, which were derived from  and 26]26], respImage pair 5 was generated using the style transfer method proposed in , in whicImage pair 6 was also generated using the method proposed in , in whicIn terms of the control experiment, we introduce the classical image local-feature descriptors SIFT, SURF, and BRSIK  as the cWe introduce the number of good matches (GM), average number of inliers (INL) per matched pair, matching accuracy (MA), and running time (RT) as the four indexes to evaluate match methods. GM measures the robustness, INL and MA measure the accuracy of the algorithm, and RT measures the real-time performance of the algorithm. For each image pair, we take the indexes average of ten test results as the final evaluation results.GM: We adopt the number of good matches in per image pair when the ratio is 0.8 to measure the adaptability robustness of the method. The larger GM obtained for each group of images, the better the performance of the matching method.INL: We take the average number of inliers in per image pair when the ratio is 0.8 to reflect the accuracy of the method, the higher the value, the better the performance.MA: We introduce the matching accuracy to reflect the effective utilization of our algorithm; MA is numerically equal to the ratio of INL to GM. To a certain extent, MA could reflect the coordination between the detector and descriptor.RT: In underwater engineering operations, real-time operation is a fixed requirement, so we introduce RT as the time evaluation index to measure the matching time, so as to verify the complexity of our algorithm.All methods were implemented under the Windows 10 operating system using Python 3.7 with an Intel Core i7-9700 3.00 GHz processor, 16 GB of physical memory, and one NVIDIA GeForce RTX2070s graphics card. SIFT, SURF, and BRISK were implemented based on openCV-Python tools . In ordeDue to the huge difference in imaging mechanism between the acoustic sensor and optical sensor, it is difficult to detect the feature points on the raw image pair. After preprocessing the raw image with image attribute transfer algorithm, the image difference is basically eliminated. At this time, dense feature points could be detected in both the acoustic domain and optical domain, and then they could be effectively matched in their respective domains. We aggregate the matching results of the acoustic image pair and the optical image pair, and mapped them to the raw image after the error elimination operation. We take one of these samples to specifically express the three processes of matching and visualize the detection result of feature points and feature areas. The detailed process is shown in The matching effects are shown in As shown in For the underwater acousto-optic image matching task, we have solved it based on the following aspects: (1) Use the image attribute transfer method to maximize the elimination of the difference between the underwater acoustic image and optical image. (2) Introduce the advanced HardNet descriptor and use the method based on local features to match the acoustic and optical images. Experimental verification: although HardNet has not been deliberately trained on underwater sonar and optical datasets, it nonetheless shows impressive results, and could overcome the viewing angle and background changes caused by the sonar detection process. The proposed method does not make any assumptions concerning the type of input acousto-optic image, nor does it require manual intervention and preprocessing—the matching process is close to end-to-end. We have achieved a relative balance of high matching accuracy and low computational complexity. Improving the efficiency of the algorithm will be the focus of our next work.A method of applying a visual attribute transfer algorithm and the learned descriptor HardNet is proposed to achieve the task of underwater acousto-optic image matching. Our method could be applied to various underwater operation scenarios and there is no need to superimpose complex sonar image processing methods. The experiment proves our proposed method could effectively solve with high accuracy and robustness the matching problem of underwater acousto-optic images.In the future, we will focus on further expanding the number of test samples and training local feature descriptors in the underwater acousto-optic images to achieve better performance. Additionally, we plan to further optimize the preprocessing algorithm for image attribute transfer to make it lightweight, in order to better meet the real-time requirements of underwater engineering operations and to enhance the autonomy of deep submersibles.


Additive manufacturing (AM), widely known as 3D-printing, builds parts by adding material in a layer-by-layer process. This tool-less procedure enables the manufacturing of porous sound absorbers with defined geometric features, however, the connection of the acoustic behavior and the material’s micro-scale structure is only known for special cases. To bridge this gap, the work presented here employs machine-learning techniques that compute acoustic material parameters (Biot parameters) from the material’s micro-scale geometry. For this purpose, a set of test specimens is used that have been developed in earlier studies. The test specimens resemble generic absorbers by a regular lattice structure based on a bar design and allow a variety of parameter variations, such as bar width, or bar height. A set of 50 test specimens is manufactured by material extrusion (MEX) with a nozzle diameter of  Porous materials are widely used for sound absorption and noise mitigation and find applications in various technical disciplines. Therein, three mechanisms can be distinguished: absorption of airborne sound, decoupling of vibrating systems and reduction of flow-induced sound. First, the most common and well-known application for porous materials is sound absorption, for example in room acoustics  or porouA common engineering task in acoustics is the adjustment of the appropriate amount of damping of a fluid cavity, for example to adjust reverberation time. Acoustic damping can be effectively reached by means of porous materials that are mounted onto the surface of a rigid wall and thus serve as acoustic absorbers. The general procedure for the application of porous material commonly is a first laboratory-scale characterization of the material followed by an analysis of the target system, resulting in information where and how the material at hand can effectively be employed. The laboratory tests mostly incorporate acoustic measurements of the absorption coefficient, flow resistivity and further parameters. This straightforward procedure allows for a reasonable application of available materials. Nevertheless, the possibility to directly design materials, in the sense of tailoring them with special regard to the application often is of interest. An approach to determine the necessary acoustic parameters that fulfill a given task is shown in  by tailoL) model ,20,21 thetermine . Indeed,etermine ,23. For etermine  the compOne promising possibility to generate acoustically effective materials is the rapidly growing field of additive manufacturing (AM), also known as 3D printing. The layer-by-layer working principle of additive manufacturing processes provides new kinds of design freedom during product development. In this way, for example, undercuts, mesoscopic lattice structures, or free-form surfaces can be realized, making additive manufacturing particularly suitable for the production of acoustically effective structures . The speDue to the aforementioned reasons, AM provides the opportunity to significantly improve both geometric and functional product properties ,28. Due this use .In the past, AM techniques have been used to produce a variety of sound-absorbing materials, including Helmholtz resonators , straighA common feature of all previously presented research results is the focus on the technical feasibility of additive manufacturing of acoustically effective porous structures and research into factors influencing the acoustic properties. However, the focus so far has been only on the manufacturing of porous geometry without defining the acoustic properties in advance. Nevertheless, the use of AM technologies for the creation of porous structures offers the possibility to efficiently introduce acoustic properties in a product-specific way, taking into account the potentials and limitations of the manufacturing process. A method for targeted adjustment of acoustic properties through defined geometries, taking into account process-related influencing factors, thus is desirable.As mentioned above, several experimental capabilities exist to investigate the acoustic behavior of porous materials. Moreover, the acoustic behavior of porous materials can be described using various mechanical models of different complexity and accuracy. Recently, AM capabilities have proven successful in manufacturing generic porous materials as well. Nevertheless, for manufacturing a porous specimen, a suitable geometric description of the specimen is required on both, the micro- and macro-scale. The acoustic description of the specimen with both, experiments and/or mechanical models, involves the description with the Biot parameters. These two “worlds”  are rather detached, as no general connection of geometric dimensions and the Biot parameters of porous materials is known. Indeed, for some special cases models exist to estimate the Biot parameters from the specimen geometry, for example, the tortuosity can be estimated from the porosity as shown in ,42,43,44A similar idea, the computation of the acoustic behavior of porous material using ML techniques, is presented, for example, in ,46,47. IThe resulting ML models are then used for an inverse material design approach. Therefore, materials are designed that are supposed to show a predefined acoustic behavior. Thereby, the advantage of using the Biot parameters as a basis becomes clear, as this procedure can be done by adjusting the specimen geometry on the micro-scale only but as well on the macro-scale.The procedure presented within this work allows to adjust the acoustic properties of the absorbers before production and thus enables an efficient application of acoustically effective materials that are best suited for the requirements of the application. In turn, this procedure might even reduce secondary factors such as costs, weight, or carbon footprint.The paper is organized as follows: in In this section, the study design and used methods are presented. The goal of the work is to build a model using ML techniques that computes the Biot parameters for a given specimen micro-scale geometry. In a subsequent step, the obtained ML models are used to design new sound-absorbing structures that exhibit a predefined acoustic behavior. Within this section, first, the overall concept of the process is described, followed by a description of the generic specimen investigated here. Furthermore, the used experimental setups for manufacturing and characterization of the specimens are described and the process to identify the Biot parameters is shown. Finally, the used machine learning models and the material design approach are described.The goal to build ML models that allow the computation of Biot parameters from a geometric description of the specimens and to utilize these for material design employs a 6-step procedure. The process is sketched in The generic specimens investigated within this work are presented in The procedure starts in Step 1 see  with a LFor the additive manufacturing of porous absorber structures, a test specimen was developed within the scope of the research work, taking design for additive manufacturing regarding design potentials and limitations into account. Detailed descriptions of the specimens baseline design can be found in . For thed), the bar spacing (s), the bar height (h) and the plane angle . Material extrusion processes offer good possibilities for the production of porous absorber structures since extremely filigree structures can be produced through changeable nozzle diameters. It is assumed that this procedure offers the highest potential that the best possible acoustic properties can be realized. However, the production of porous absorber structures also places special demands on the manufacturing process, since the use of fine nozzle diameters  and manufactured on the X400, a pro-consumer additive manufacturing machine from German RepRap GmbH , whose original extruders were replaced by E3D Hemera . A In addition, compared to other processes, such as PBF-P or VAT, there is the advantage that there is no material buildup due to sintering or unwanted polymerization due to capillary effects . A total out see .The method presented within this work aims to train ML models to map between the geometry and the Biot parameters of porous samples. The geometry of all manufactured specimens is known from the manufacturing process. Indeed, this manufacturing process is somewhat uncertain, hence the manufactured geometry probably differs from the design parameters. Nevertheless, previous studies have shown that the manufacturing process applied here at least allows a sufficient precision for the purpose of this work . Apart fq and the pressure drop over the specimen R can be directly evaluated by A and the specimen thickness l by All 50 manufactured specimens, as described in The absorption coefficient is measured in an impedance tube with a diameter of 30 mm using the two-microphone method according to ISO 10534-2:1998 . The meaBased on the measured flow resistivity and absorption coefficient data described in The JCAL model allows the computation of the equivalent density quations  and A2)ρ˜ and thquations  and (2) l is used, With the quantities, Equation  from [65Here, it becomes clear why the prediction of the Biot parameters is preferred over the direct prediction of the absorption coefficient. The Biot parameters (or the subset used here for the JCAL model) are material-specific parameters. The acoustic behavior of the material for the case of a material mounted in front of a rigid wall is accounted for in subsequent computation. Hence, the process proposed here finally allows the description of the material at hand, independent of the mounting conditions or the sample thickness. Using the surface impedance Using the Equations –4), , i:As described before, the flow resistivity is determined experimentally, the remaining five parameters  of the manufactured 50 specimens. The inverse parameter identification procedure is run ten times for each specimen, hence the database that can be used for training the ML models comprises 500 data sets with slightly different geometry-Biot parameter combinations. This behavior is due to the fact that the inverse parameter identification problem is ill-posed. Therefore, without additional information, it cannot be distinguished between the different results and whether one or the other outcome is the physically correct result and hence, all available results are used. In −1. Therefore, it is assumed that indeed some differences occur for each run but since the deviations are rather small, the general behavior is kept. Thus, it is assumed that the data augmentation yields meaningful results to enrich the database and the choice of input parameters is reasonable.It can be seen, that a general correlation . The basic idea of ML is, in contrast to this classical model building approach, that the relations between the model parameters are not described based on physical laws and mathematical equations, but that the relations are derived from existing data. This process of deriving the relations from data often is referred to as learning or training the ML model. Thereby, it is of tremendous importance that the desired relations are already “coded” within the existing data. The field of ML models can generally be divided into the sub-classes of supervised and unsupervised learning models. Thereby, supervised learning employs both, the input data for the model and so-called labels, which is the desired output of the model. On the other side, unsupervised learning models require the input data only and attempt to find some sort of structure within this data, for example, clustering.Within the scope of this work, two different types of ML models are employed: (a) a K-Nearest Neighbor (KNN) model, see −1, the magnitude of the plane angle is in the order of 1 × 101. The input data is scaled using mean and standard deviation of the data with:The input data for the ML models are geometry parameters and, for the bar width, the bar spacing and the bar height measured in mm. The plane angle in measured in y represents the standard deviation of the input data.In Equation , the qua3–1 × 105, the magnitude of the static thermal permeability is approx. 1 × 10−8. It could be found that the training process becomes rather unsuccessful when the input and output data is used directly. This is expected to be a result of the large range of values within the data. Therefore, variable scaling is introduced and applied to the Biot parameters in order to result in training data comprising a smaller value range. The resulting scaled Biot parameters are shown in The Biot parameters, which is the output data of the ML models, have very different dimensions and scales, for example, the viscous and thermal characteristic lengths are geometric lengths, the static thermal permeability has the dimension of a surface, the porosity and tortuosity are dimensionless and the flow resistivity has the dimension The flow resistivity remains unscaled. This scaling is assumed to only improved the training performance of the ML models but does not affect the general procedure.k) that are accounted for during the classification process. Due to expected noise within the data, it is common to compare new data instances to more than one instance. The parameter k determines the number of data sets to which the new data instance is compared. The distance measure and the number of neighbors are the so-called hyperparameters of the ML model. The appropriate choice of these hyperparameters is crucial for a successful application of ML models. KNN models can be applied to regression problems as well. In such cases, the output quantity is not a single and discrete class but is computed as a continuous quantity from the k neighbors [K-Nearest Neighbor (KNN) ML models are a very popular approach and have been successfully applied to classification problems. For further information on the topic, the reader is referred to the the vast amount of textbooks and papers on this topic, such as ,72,73,74eighbors .k-parameter of k-parameter results from the hyperparameter tuning that was done on the validation data set. In k-parameters are shown. It can be seen, that the cross-validation score is improved from k do not further improve the score. Therefore, The KNN model employed within this work uses a The approach of the Artificial Neural Networks is based on the idea of the so-called perceptron ,76. A peThe ANN employed within this work is set up as a four-layer feed-forward ANN with two hidden layers. The input and output layers have 4 and 6 neurons, respectively, according to four geometry parameters that serve as inputs and the six Biot parameters The hyperparameters  are derived during a 3-fold cross-validation procedure. Thereby, the topology mentioned above (4-4000-100-6) reached the best validation score, as shown in Within this section, the obtained results of the work are presented. In the first part , the manl and prediction of the absorption coefficient for a broader frequency range. Height and frequency range were kept constant for the initial specimen population with which the Biot parameters are identified. Now the Biot parameters that were obtained by the ML models are used to predict the absorption coefficient of a specimen with a larger specimen height and for a broader frequency range. This shows, that the obtained models are suitable as well to design porous absorbers with a different geometry.The overall idea of the procedure within this work is to generate models that connect the specimen geometry to the Biot parameters. The reason for choosing the Biot parameters  rather than directly predicting the behavior of the material, for example, the absorption coefficient, is that the Biot parameters can be used for subsequent analyses and other contexts. This is addressed in Within this section, the results of the inspection of the initially manufactured specimen population are shown. Thereby, in To assess the manufacturing accuracy of the additively manufactured test specimens, an actual-target comparison of the initial geometry and the manufactured test specimen was carried out. Specimens 2 and 39 are selected as examples since these specimens show the highest and lowest mean absorption coefficient over the frequency range of interest see b and thuTest specimens 2 and 39 are generated using the variation parameters given in Test specimen 2 shows a relatively uniform production pattern, with a partial tapering of the filament strands in the spaces between the underlying bars. Two applied filament strands each form the individual webs of the acoustically effective structures, the width of which is A similar manufacturing analysis was also carried out for test specimen 39, see All in all, the manufacturing accuracy can be classified as acceptable and only slightly fails to meet the desired dimensions. The highest measured deviation is at a bar width of The results of the measurement of the flow resistivity are shown for all 50 specimens in These specimen exhibits the largest (specimen 2) and lowest (specimen 39) mean absorption coefficient over the frequency range, as shown in In The absorption coefficient curves shown in X and Y, respectively) for the two quantities are computed and given in the plots.Based on the measured flow resistivity and absorption coefficient, the Biot parameters are inversely derived using the procedure described in For the bar width,  and a test data set (B). The test data set comprises 20% (100 data instances) of the available data and is used for the performance test at the end of the training process, only. The test data set is chosen randomly from the available 500 data instances. The hyperparameters, namely the number of nearest neighbors and the distance measure of the KNN model and the number of layers/neurons and activation function of the ANN model, were found using a three-fold cross-validation schema . TherefoR2 . This quR2 . The R2 k-parameter of larger than The cross-validation procedure is run several times with varying hyperparameter in order to find parameters that perform well , , After completing the material design using the process described here, the resulting designed absorbers are printed equivalently to the specimen used for the data generation. The resulting specimens are measured in the impedance tube regarding their absorption coefficient and using a 3D surface profilometer regarding their geometry. The results are presented in the following sections.The first attempt for generating printable porous absorbers that show a prescribed absorption characteristic is done using the KNN model. The design parameters for all three target curves and the corresponding measured values are shown in In The results of the predicted and measured absorption coefficient are shown in For the target curve “low”, see It should be noted here that all measured absorption coefficient curves show a rather strange behavior above 6200 Hz, as the absorption coefficient curves show some sort of “kink” and increases for larger frequencies, whereas it monotonically decreases for lower frequencies. This behavior is not common for porous absorbers since such materials are expected to show a rather smooth course of the absorption coefficient over frequency. It is expected here that this “kink” is caused by an imperfect fit of the specimen within the impedance tube that leads to enclosed air-filled voids between the specimen and the tube’s wall. Such voids might serve as a high-frequency resonator, resulting in a corrupted absorption coefficient measurement. The imperfect fit of the specimen again might be due to manufacturing inaccuracies.To summarize, the KNN model can generate printable geometry descriptions for the design of porous media. Nevertheless, it is required that the requested designs lie somehow within the previously learned data range, the ability to extrapolate outside the learned data range seems rather limited.The material design task using the ANN model leads to rather different designs compared to the KNN model. The predicted parameter values for all geometry parameters and target curves are shown in It can be seen that generally similar parameter ranges as obtained with the KNN model are reached. The manufacturing accuracy as well is similar to the KNN model, the maximum deviation between the manufactured and predicted geometry parameters is In A rather undesirable result is shown in For the target curve “high”, the results are shown in Regardless of the deviations in the high-frequency range, The ML models are built to predict the Biot parameters from the specimen’s micro-scale geometry. Thus, material parameters of the porous material are obtained. The “detour” via the Biot parameters opens the opportunity to design other specimens with varying macro-scale geometries, such as the specimen height. Moreover, the possibility to predict the acoustic behavior for other application cases, for example other frequency ranges becomes possible. This is due to the fact that Biot parameters are material-specific parameters and thus no mixing of the material description and the acoustic behavior of the material in specific conditions  occurs. Therefore is assumed that, if the Biot parameters identified on specimens with a small height can be used as well to predict the behavior of specimen with a larger height, the Biot parameters can be trusted as they give a reasonable description of the material.In order to verify this assumption, the design parameters of the specimen with target curve “high”, computed with the KNN model are used, see In The presented work aims to bridge the gap between the micro-scale geometry and acoustic material parameters of porous absorbers to allow a reasonable absorber design. The core challenge is that porous absorbers can be characterized from an acoustics point of view utilizing the Biot parameters and by geometric dimensions on the micro-scale. These two “worlds” are separated, the estimation of the Biot parameters from the geometry is possible only for simple special cases. The work presented here shows a way of connecting these two “worlds” through machine learning. In the following, the work is briefly summarized and an outlook on future works is given.The presented work investigates a generic test specimen constructed by a micro-lattice of parallel bars. The specimens are manufactured using additive manufacturing, a specifically appropriate production procedure for porous materials. In order to connect the micro-scale geometry and the acoustic material parameters, namely the Biot parameters, the range of reasonable and manufacturable geometry values is sampled using a Latin Hypercube Strategy and an amount of 50 specimens is manufactured. The resulting specimens are optically and acoustically investigated, the latter comprising measurements of the absorption coefficient and flow resistivity. The Biot parameters of the specimens are derived using an inverse parameter identification scheme. Using the geometry of the specimens as inputs (features) and the Biot parameters as outputs (labels) two Machine-Learning regression models (K-Nearest Neighbors (KNN) and Artificial Neural Network (ANN)) are built and trained by means of supervised learning. The resulting models are then applied to a material design procedure with the goal of generating printable absorber structures that exhibit predefined absorption characteristics. This procedure finally is capable of computing the micro-scale geometry of the specimens that is required to obtain a porous absorber that shows the desired acoustic behavior, which is measured here in terms of the absorption coefficient.The approach of using the Biot parameters here to design porous absorbers is chosen, as it exhibits some favorable implications. The Biot parameters can be viewed as material-specific parameters of the porous material and thus are invariant to the macro-geometry (for example the thickness) of the material, the mounting conditions or the sound field. If a quantity describing the behavior of the material was considered, for example, the absorption coefficient, a mixing of material description and material behavior would occur. This mixing is circumvented by predicting the Biot parameters directly and thus, these parameters can be used for other subsequent analyses as well without the limitation on how the parameters were obtained before.A first aspect of the work presented here is the additive manufacturing of the specimen geometry, especially on the micro-scale. Here, a material extrusion process is employed, thereby specific means as targeted under-extrusion and the use of a The machine-learning models are set up using the micro-scale geometry of the specimens as input and the Biot parameters as outputs. A correlation analysis shows that the models investigated here, namely the K-Nearest Neighbor model and the Artificial Neural Network are able to reasonably compute the Biot parameters from the specimen geometry. The obtained models are then applied to design porous absorbers that show a predefined acoustic behavior, here characterized by the absorption coefficient. Therefore, target curves of the absorption coefficient are defined and the specimen’ micro-scale geometry is inversely computed. It can be shown, that both models are able to generate printable absorber structures. However, some differences can be observed. A major difference between the models is their ability to extrapolate from the trained data—the KNN model is not able to produce specimens beyond the initial population, whereas the ANN model is able to compute designs that lie outside the initially learned data range. However, the measured absorption coefficient of the designed specimens matches the prediction in all three cases for the KNN model, the ANN model is able to predict the absorption coefficient only for two of three cases correctly. Finally, the procedure is applied to predict and generate another specimen with a different macro-geometry  as well. It can be shown that this application case can be handled by the proposed procedure fairly well.Summarizing, the procedure to generate machine-learning based models that predict Biot parameters from porous absorbers micro-scale geometry can be viewed as successful. It was possible to generate models that are capable of the intended task and that these models can be successfully applied to design new absorbing structures. The higher effort for obtaining Biot parameters and thus focusing on the material description rather than on the acoustic behavior allow the application of the procedure for a multitude of practical applications, such as the generation of input parameters for finite element analyses or the idea of tailoring materials specifically for their intended application case.Although the presented process already delivers good results, some open questions remain. These will be hereafter discussed. For instance, the design of the presented work is rather complex, which is mainly due to the necessary computation of the Biot parameters. Therefore, it would be feasible to re-conduct the study and to measure more of the Biot parameters directly. This would result in a lower dimension of the optimization problem and probably more accurate results of the inverse parameter identification of the remaining Biot parameters. Another aspect for future works is the fact that not all prediction results of the machine-learning models could be verified by measurements. For example, it should be clarified why the result obtained with the ANN for the target curve “medium” shows high deviations compared to the other results. Moreover, it is assumed that the specimen design induces some physical limit that prevents from obtaining an even better acoustic behavior. The exploration of these limits is also subject to future work.Another aspect regarding the application of the proposed method is the exploration of the physical relation of the Biot parameters and the specimen’s geometry. As already stated, for some special cases the Biot parameters can be estimated from the material micro-scale. This will be further investigated using the models obtained within this work and indeed is planned as the next step.There is also a need for further research in the field of additive manufacturing processes, for example in the field of microstructures. An extended understanding of the dependencies between process parameters and the resulting microstructure is important. This knowledge would also establish the connection between deviations in the shape and form of the microstructure and resulting acoustic properties. The derivation of design rules for the manufacturing of microstructures utilizing material extrusion is necessary to ensure the realization of structures that are as small as possible. The topic of robust additive manufacturing of microstructures also has great research potential.In addition, there is also a need for further research in the area of the resulting dimensional accuracy and also the shape of the additively manufactured microstructures.


Haze has been a severe problem in China for some time, jeopardizing air quality, public health and sustainable growth. This paper examines the direct effect and spatial spillover effect of policy uncertainty on haze pollution with a spatial panel model, using prefecture-level data from 2004 to 2016. This study shows that: (1) policy uncertainty has increased the level of local haze pollution and has a significant spatial spillover effect on surrounding areas; (2) although local policy uncertainty has increased the haze pollution in geographically adjacent cities, it only affects the cities within the province with similar economic distances; and (3) the policy at the central level can effectively alleviate the impact of policy uncertainty at the local level on haze pollution, especially in relation to the spatial spillover effect, but still has limitations in eliminating the direct effect, which is due to the ineradicable nature of policy uncertainty. If Moran’s I index is close to 1, it represents the positive spatial autocorrelation of cross-regional haze pollution. If it is close to -1, it represents the negative spatial autocorrelation. If it is close to 0 or not statistically significant, there is no spatial correlation.Before construction of the spatial econometric model, a test was performed to verify the spatial autocorrelation of the data. By constructing the reciprocal matrix of distance, this paper utilizes Moran’s I index to measure the spatial autocorrelation characteristics of PMTo introduce spatial variables into our econometric framework, this paper applies the spatial Durbin model, which is a generalized spatial model compared with the spatial lag model and the spatial error model ,34. The The first three columns in Column 2 reports the spatial spillover effect. Local policy uncertainty has a positive impact on haze pollution in neighboring cities, and this impact is statistically significant at the 5% level. The spatial spillover effect is very large. To ensure the robustness of the conclusion, this paper further uses the adjacency matrix instead of the reciprocal distance matrix to re-estimate and finds that the magnitude and significance of the coefficients remain unchanged. Overall, the spatial spillover effect accounts for most of the total effects in the model.After the spatial spillover effect of haze pollution due to policy uncertainty is uncovered, the next question lies in identifying the mechanisms through which spatial spillover occurs. According to the existing literature, haze pollution can be diffused through geographical mechanisms, such as atmospheric circulation, or non-geographical mechanisms, such as a regional race to the bottom in emissions to raise GDP output. However, it is a major challenge to separate the above two mechanisms empirically. Fortunately, the unique performance review system for Chinese officials, “one-level-down management”, makes it possible to differentiate the two mechanisms. Under this arrangement, the competitors of prefecture-level city officials are confined to those both within the same province and with a similar economic development level . TherefoAfter construction of the matrices, this paper proceeds to segregate geographical and non-geographical mechanisms. The identification logic is as follows. If the geographical mechanism dominates, air pollution diffusion would be observed in geologically adjacent cities regardless of whether they are within or outside of a province. In other words, the spillover effect will manifest both with an intra-provincial spatial matrix and inter-provincial spatial matrix based on geographical distance. If the non-geographical mechanism dominates, such as the race-to-the-bottom emission, the existence of the spatial spillover effect would concentrate only in cities with similar economic levels within the province. This is because intra-provincial officials are motivated to connive at emissions in the competition of GDP output under the one-level-down management system. At the same time, the spillover effect would be statistically insignificant in the inter-provincial matrix with economic distance because officials are not in the same ranking pool.The analysis in To understand the effectiveness of policies from the central government, this paper examines the effect of policy uncertainty before and after the release and implementation of the unified and strict measurement “Measures for the Assessment of the Implementation of the Air Pollution Prevention and Control Action Plan ”, which is the strictest system for atmospheric environment management responsibility and assessment.2.5 and PM10 indicators should be taken as binding indicators in order to establish a target-responsibility assessment system, which takes environmental quality improvement as the core. Six departments including the Ministry of Environmental Protection issued the “Measures for the Assessment of the Implementation of the Air Pollution Prevention and Control Action Plan ”, which clarified the evaluation content, evaluation method, evaluation procedure, and disciplinary measures of atmospheric pollution prevention and control.In September 2013, the State Council issued the Notice on the Issuance of the Action Plan for The Prevention and Control of Haze Pollution, proposing that PMThe publication of these policies marks the establishment of the Chinese atmospheric environmental management system. According to the measures, the central government will increase support for regions with excellent assessment results. In contrast, local governments that fail to pass the assessment will obtain fewer funds from the central government. Moreover, they will be questioned by the Ministry of Environmental Protection together with organizations, such as supervisory departments.Because the assessment method clearly states that performance will not be assessed in 2013, this paper divides the sample into two sub-samples with two time periods: 2004~2013 and 2014~2016. This paper separately investigates the effects of policy uncertainty on haze pollution before and after the implementation of the central level policy that includes “haze reduction”.Panel A in These results indicate that the assessment policy from the central government regulates the behavior of local officials with clearer assessment indicators and severe punishment measures. This regulation only marginally alleviates the impact of policy uncertainty on haze pollution. This result suggests that unifying the assessment alone may generate a very limited policy effect.There may be endogeneity problems caused by missing variables and reverse causality in this paper. Although studies have shown that the spatial Durbin model has advantages in dealing with endogeneity problems caused by missing variables , a betteThe existing studies use two main approaches to select GMM’s instrumental variables: one is to use official age and tenure as instrumental variables for the replacement of officials; the other is to use lagged variables. Due to the fact that the number of official turnovers is used to measure the level of policy uncertainty, it is not appropriate to use the individual characteristics of an official as the instrumental variable. This paper takes the one-period lagged official turnover variable as the instrumental variable. The estimated results in This paper tests the robustness of the measurement of policy uncertainty using an indicator variable for officials’ turnover (uncertainty_1) to measure policy uncertainty and re-estimate its impact on haze pollution. It takes the value 1 if there is a change in the party secretary or mayor of a prefecture-level city once within a year and the value 0 otherwise. In addition, given that the local government power is concentrated in local party committees, party secretary turnover (uncertainty_2) is used as an indicator of the level of policy uncertainty. It takes the value 1 if there has been a change in the party secretary in the current year. This study investigates the impact of policy uncertainty on haze pollution and its spatial spillover effect based on data from prefecture-level cities. The spatial Durbin model is constructed to analyze the effects of policy uncertainty on haze pollution. The research demonstrates that policy uncertainty will exacerbate the local pollution level while significantly spreading over surrounding areas. The effect could be explained by investment expansion, regulatory gaps and responsibility gaps, as a result of policy uncertainty.Furthermore, geographical distance and economic distance are used to construct intra-provincial and inter-provincial spatial matrices. The matrices assist with our understanding of the potential mechanisms of the spatial spillover effect of haze pollution caused by policy uncertainty. The results show that: if the spatial matrix is constructed based on geographical distance, local policy uncertainty will aggravate haze pollution in the surrounding cities regardless of whether they belong to the same province or not. If the spatial weight matrix is constructed based on economic distance, local policy uncertainty will affect haze pollution only in cities with similar economic distance within the province, but will have no impact on cities in other provinces. The results unveil the dual mechanisms of pollution spatial spillover: one is a geographical mechanism, such as atmospheric circulation, and the other is non-geographical mechanism, such as a race-to-bottom emission among intra-provincial cities with similar levels of economic development.Last, the paper examines whether central government policies with higher authority can suppress the impact of local policy uncertainty on haze pollution. By examining the implementation of “Measures for the Assessment of the Implementation of the Air Pollution Prevention and Control Action Plan ”, this paper concludes that central government intervention has a significant positive effect on reducing the impact of policy uncertainty on local haze pollution, but still has limitations in terms of eliminating the direct effect, which is due to the ineradicable nature of policy uncertainty.The policy implications of the above conclusions are listed below. First, there is a need to systematize the haze control approach to minimize haze policy uncertainty and its implications for both local haze pollution and spatial spillover. At the same time, since policy uncertainty is unavoidable by nature, systematizing the haze control approach will help to abate the positive impact of policy uncertainty on pollution. Second, it is recommended to optimize the performance review system for government officials to avoid the phenomenon of beggar-thy-neighbor emissions. A two-pronged approach could be considered—to strengthen the monitoring of haze pollution along the province borders and to establish a more reasonable performance review system to avoid the race-to-bottom emission competition. Third, policymakers should fully examine the incentive and pragmatic constraints of all parties when they aim to reduce the impact of policy uncertainty. In short, the fundamental solution for curbing the adverse impact of policy uncertainty on haze pollution control is to foster a stable strategy towards haze control within local governments.


The direct-sequence spread-spectrum (DSSS) technique has been widely used in wireless secure communications. In this technique, the baseband signal is spread over a wider bandwidth using pseudo-random sequences to avoid interference or interception. In this paper, the authors propose methods to adaptively detect the DSSS signals based on knowledge-enhanced compressive measurements and artificial neural networks. Compared with the conventional non-compressive detection system, the compressive detection framework can achieve a reasonable balance between detection performance and sampling hardware cost. In contrast to the existing compressive sampling techniques, the proposed methods are shown to enable adaptive measurement kernel design with high efficiency. Through the theoretical analysis and the simulation results, the proposed adaptive compressive detection methods are also demonstrated to provide significantly enhanced detection performance efficiently, compared to their counterpart with the conventional random measurement kernels. In this era, the direct-sequence spread-spectrum (DSSS) has become one of the most widely used spread-spectrum (SS) techniques in the wireless secure communications . In DSSSThe detection of the DSSS signals is the prerequisite to the following signal processing and information extraction steps ,4. It haIn last decade, the compressive sensing (CS) theorem was rendered ,9, whichPresently, with the rapid development of the computer technologies, especially the parallel computing, the artificial neural network  and the deep learning  techniquIn this paper, we propose methods to detect the DSSS non-cooperatively and adaptively based on knowledge-enhanced compressive measurements and artificial neural networks. The measurements are done with compressive rates to reduce the costs of sampling process and the detection decisions are made based on measurement energy thresholding. The detection task-specific information (TSI) quantitative analysis with the signal posterior probability updates is introduced in the adaptive measurement design to improve the detection accuracy. To greatly improve the efficiency of the algorithm, the artificial neural networks are trained based on the TSI optimization. The resulting neural networks can take the posterior probabilities of the signals from the Bayes updates as the inputs and directly give the adaptively designed measurement kernels.(1)Compared to the existing compressive detection methods, the proposed methods enable an adaptive compressive measurement framework, where the measurement kernels can be flexibly adjusted to track the DSSS signals without the exact prior knowledge of the PN sequences in the detection.(2)To ensure the gain of the detection accuracy, the quantitative information analysis from the previous measurements is implemented in the following adaptive measurement matrix design, ensuring the gradually increased correlation to the most probable signals.(3)Through the effective combination of knowledge-enhanced compressive measurement with TSI optimization and the artificial neural network techniques, the compressive measurement matrix can be designed in an both adaptive and efficient manner. Compared to the recursive measurement kernels directly optimized based on quantitative information analysis in the literature, the artificial neural networks are trained based on TSI optimization off-line and implemented repeatedly and efficiently in the online adaptive measurement kernel design, which not only improves the adaptability, but also saves a lot of detection time.Our work makes several novel contributions:From the aspect of the signal processing systems, with the proposed method, both the efficiency of the adaptive measurement system and the adaptability of the neural network-based system are achieved.The remainder of the paper is organized as follows: In With the rapid development of the communication technology, spread-spectrum communication has become an important way in the modern communication system. DSSS communication system has been widely used in the military and civil communication domain. From the electronic countermeasure perspective, to intercept or interfere signals that may be transmitted in the DSSS mode, it is necessary to first detect whether there is a DSSS signal present in the wireless channel, before finally recovering the information contained in the signal. Therefore, the detection of the DSSS signals is indispensable in the entire DSSS signal reception process.Due to the importance of the DSSS signal detection step, a lot of research has been done in this area with a series of detection methods proposed, which can be basically classified as non-compressive and compressive detection methods. In the following part of this section, the two types of methods are introduced as the background of the DSSS signal detection techniques.As is implemented in conventional methods of the signal processing, the signals were sampled according to the Nyquist sampling rate to capture their entire spectrum and avoid aliasing. Before the CS theory was rendered, researchers proposed many non-compressive detection methods, such as energy-based detection methods , auto-coBack to the early 1960s, H. Urkowitz proposed the energy-based detection method , where tThe autocorrelation-based detection methods ,19 were The spectrum-based detection methods  theorem ,9 was reMost of the existing CS-based DSSS signal detection methods were proposed based on random measurement kernels and CS recovery methods. Some of these methods retained the information carried by a signal, which could be sparsely represented based on a transform or a dictionary ,27,28,29Although most of the literature on CS used random measurement kernels, Gu et al.  and NeifIn this paper, we propose methods to detect adaptive DSSS signals based on knowledge-enhanced compressive measurements and artificial neural networks. With the compressive measurements, the hardware burden caused by the non-compressive detection methods are solved. The detection decisions of the DSSS signals are made by the observation of the measurement energy, which is easier and less expensive than most of the compressive measurement-based detection methods. Moreover, with posterior knowledge of the signal updated and the implementations of the artificial neural networks, the measurement kernels are designed adaptively and efficiently with the quantitative TSI optimized, leading to improved detection performance.The proposed compressive measurement and detection framework is shown in In this paper, we focus on the non-fading communication channels and the signal detection using the framework in In this paper, we take the phase-shift-keying (PSK) DSSS signals as examples, which can be represented as:In Equation , c(t) isM can be expressed as:In this paper, we model the wireless channel as additive white Gaussian noise (AWGN) channel over the DSSS spectrum with the noise variance of g theory , the meaIf the coefficients in each single block of the measurement matrix are randomly selected from the identically independent complex Gaussian distributions and normalized to unit energy, the DSSS signal can also be modeled as AWGN over the DSSS spectrum in the signal detection scenario. Then in the signal present case, the theoretical PDF of the energy in an M-length measurement vector can be expressed as:T, the theoretical false positive rate  sequentially, the measurement kernel for the During the operation period of According to the information theory,Equation  is equivL is the number of possible PN sequences, and lth PN sequence is used in the DSSS signal present case, given the measurement kernels and data in the 1st through the mth row of the measurement matrix. In this paper, we focus on short-code DSSS (SC-DSSS) signals, where the period of the PN sequence is equal to the symbol period. In the case of measurements within symbol period, the measurement kernel een used ,36. In mWith a simplified assumption that the single measurements are independent to each other, Equation  can be oWith the MoG signal and AWGN channel models, if the rows of the compressive measurement matrix are normalized, it can be further proved that the signal absent case can be ignored in the optimization problem. Therefore, Equation  can be dkth iteration is performed using:In the literature, to solve an optimization problem such as Equation , a recurThe derivations to Equations , 15) an an15) anTo get the convergence of the recursive method described in Equation  and achiTo improve the algorithm efficiency, we propose to implement the artificial neural networks to conduct the adaptive measurement kernel design. The architecture of the neural networks is described in In Equation . The neuIn this paper, two neural network strategies are proposed for the adaptive measurement kernel design. In the first strategy, independent neural networks are trained to design the measurement kernels for different measurements in a single symbol period, respectively. Thus, the number of neural networks to be trained is equal to the number of measurements for a symbol period. In the second strategy, a single neural network that can be used to get the measurement kernels for all the measurements in a symbol period is designed. In this case, only a part of the designed coefficients are used for each measurement. To be concise, the two neural network strategies above are referred to as “multiple neural network strategy” and “single neural network strategy” in the remainder of this paper.mth measurement in the multiple neural network strategy, the training penalty function is taken to be the negativity of the conditional differential entropy in Equation  modulated SC-DSSS signals in the theoretical analysis and simulations. The possible candidate PN sequences were taken from the maximum-length sequences (m-sequences) ,38 of thN, where not all the entries are zero-valued. In The maximum length of the m-sequences specified in Both multiple neural network strategy and the single neural network strategy described in The neural networks were optimized using the TensorFlow 2.0 GPU version  based onIn the simulations, we define the SNR as the ratio between the signal power and the noise variance, i.e.,As is discussed in As is specified above, in the theoretical analysis and simulations of the measurements and detection within single symbol period, the number of Nyquist samples was taken to be 62. To conduct compressive measurements, the number of compression measurements for detection were chosen from 6, 9 and 12 in this part, resulting in the We first analyzed the theoretical detection accuracies of the proposed methods through single symbol-period measurements. As the adaptive measurement processes are stochastic with feedbacks, it is difficult to analyze their theoretical detection performance with closed formulas. To surrogate, we took an approximation, where the PN sequence used in the DSSS signals was exactly known in each detection and the posterior probabilities of the PN sequence usage in each measurement kernel adaptation were given as a binary 1-sparse vector. In this case, we ran Monte-Carlo simulations with the quations . ConsequFor comparison, the theoretical performance of the non-compressive energy detection method and the conventional compressive detection method with random measurement kernels at the 3 quations . The anaFrom For the proposed adaptive methods, if we compare the multiple neural network and single neural network strategies, we observe that the adaptive method with the multiple neural network strategy shows slightly better performance in the detection accuracy than the adaptive method with single neural network strategy. As a trade-off, a higher cost in the hardware and network training time is introduced by the multiple neural network strategy.Besides the theoretical analysis, the Monte-Carlo simulations of the DSSS signals detection using the proposed adaptive methods were also performed for the 3 Comparing mth row of the measurement matrix and the used PN sequence  is defined by:mth row of the measurement matrix vth dictionary atom discussed in the MoG model in To validate the discussions above and have a deeper insight into the proposed adaptive methods, we conducted a further study on the correlations between the rows of the designed measurement matrix and the PN sequence that was factually used in the DSSS signal generation. In this paper, the correlation between the fined by:ξm=|<Am,bAs a representative, In Besides the studies on the detection accuracies and the measurement procedures of the proposed adaptive methods, we also conducted a study on the time costs of the proposed adaptive measurement kernel design methods based on artificial neural networks to observe their efficiencies. To validate, the time cost of the recursive optimization method described in From It has been discussed in In For the simulation results in In this paper, we proposed adaptive methods to measure and detect the DSSS signals using knowledge-enhanced compressive measurements. The detection was done based on energy detection and the measurement matrix was designed adaptively based on TSI optimization. To improve the measurement design efficiency and make the system feasible, the artificial neural networks were trained and implemented in the adaptive measurement kernel design, as a surrogate of the recursive optimization method in the literature. Theoretical analysis and simulations were performed to compare the proposed methods, the non-compressive detection method and the conventional compressive detection method using random measurement kernels. The theoretical and simulation results demonstrated that the proposed methods provided significantly enhanced detection accuracies with high efficiency, compared to the conventional compressive detection method with random measurement kernels.