Biomarker detection presents itself as a major means of translating biological data into clinical applications. Due to the recent advances in high throughput sequencing technologies, an increased number of metagenomics studies have suggested the dysbiosis in microbial communities as potential biomarker for certain diseases. The reproducibility of the results drawn from metagenomic data is crucial for clinical applications and to prevent incorrect biological conclusions. The variability in the sample size and the subjects participating in the experiments induce diversity, which may drastically change the outcome of biomarker detection algorithms. Therefore, a robust biomarker detection algorithm that ensures the consistency of the results irrespective of the natural diversity present in the samples is needed.Toward this end, this paper proposes a novel Regularized Low Rank-Sparse Decomposition (RegLRSD) algorithm. RegLRSD models the bacterial abundance data as a superposition between a sparse matrix and a low-rank matrix, which account for the differentially and non-differentially abundant microbes, respectively. Hence, the biomarker detection problem is cast as a matrix decomposition problem. In order to yield more consistent and solid biological conclusions, RegLRSD incorporates the prior knowledge that the irrelevant microbes do not exhibit significant variation between samples belonging to different phenotypes. Moreover, an efficient algorithm to extract the sparse matrix is proposed. Comprehensive comparisons of RegLRSD with the state-of-the-art algorithms on three realistic datasets are presented. The obtained results demonstrate that RegLRSD consistently outperforms the other algorithms in terms of reproducibility performance and provides a marker list with high classification accuracy.The proposed RegLRSD algorithm for biomarker detection provides high reproducibility and classification accuracy performance regardless of the dataset complexity and the number of selected biomarkers. This renders RegLRSD as a reliable and powerful tool for identifying potential metagenomic biomarkers. Thanks to the progress witnessed by the high-throughput sequencing technologies, large-scale investigation of bacterial collectivities has become possible by means of metagenomic approaches. This large-scale analysis lead to the discovery of bacterial groups that could not be analyzed through the conventional cultivation-based methods . In Recently, several metagenomic studies have pointed out that the distortion of the normbiosis state of bacterial communities is a key player in the progression of many diseases such as obesity –7, diabeBiomarker detection is crucial to understand disease development and design antibiotic and/or probiotic therapies. Mathematically, the task of biomarker identification can be formulated as determining the most revealing features that can differentiate multiple sets of samples or conditions . The methods proposed in literature to address the biomarker discovery problem can be classified into two categories: machine learning (pattern recognition) methods and statistical methods, respectively.In general, the statistical approaches tackle the problem by using a statistical hypothesis test to calculate the statistical significance  of each feature. Then, the features associated with p-values lower than a well-selected level are declared as potential biomarkers. A major issue linked with the statistical-based methods is the multiple comparisons problem, which is commonly solved by substituting the p-values with the corresponding false discovery rates (FDRs). Metastats  and LEfSIn the machine learning framework, the problem of detecting the biomarkers is formulated as a feature determination task. The filtering methods are the most widely adopted approaches for biomarker detection. In filtering methods, each OTU is assigned a score based on the relevance between its abundance levels across the samples and the class labels of the samples. The operational taxonomic units that present the largest scores are declared as potential biomarkers. This scoring process is carried out one by one for each OTU and separately of the other OTUs. Therefore, filtering methods are computationally fast and easily interpretable. However, the individual ranking ignores the inter-dependencies among different variables.Contrary to the individual ranking, the feature transformation-based methods try to generate more revealing features where each newly detected feature is depedent of all the original features. Considering all the initial characteristics in the construction of new traits accounts for the interactions between OTUs. Transformation approaches are divided broadly into two categories based on whether the labels of the samples are considered in the transformation process. These categories are the supervised and unsupervised approaches. Linear discriminant analysis (LDA) and partial least-squares (PLS) represent the two most employed supervised approaches. On the other hand, the principal component analysis (PCA) presents itself as one of the most remarkable unsupervised methods.Identifying the most discriminating features in metagenomic datasets is a challenging task. One major challenge is that the number of biomarkers might be much larger than the number of available samples, a condition that it is commonly termed as the ‘high dimension low-sample size (HDLSS)’ problem. The HDLSS problem is also associated with serious analytical challenges , 15. In These challenges point to a severe inconsistency issue that blocks the current biomarker identification methods from selecting the true biomarkers. For example, the authors of  reportedToward this end, we propose herein paper the Regularized Low Rank-Sparse Decomposition (RegLRSD) algorithm for biomarker detection. RegLRSD formulates the biomarker discovery problem as a matrix decomposition problem and provides an efficient solution for this decomposition. In particular, RegLRSD models the bacterial abundance data as the superposition of a sparse matrix and a low-rank matrix. The motivation for this is due to the fact that most of microbes do not play any role. Hence, the abundance profiles of these uninformative bacteria do not vary between samples associated with different phenotypes. Therefore, considering their abundance profile as a low-rank matrix is natural. In addition, few microbes may be relevant to the biological condition under study. Consequently, the abundance profiles of these relevant microbes are expected to vary significantly between the different phenotypes. Therefore, modeling these informative bacteria as a sparse matrix is legitimate.To improve the accuracy of extracting the low-rank and sparse matrices, we exploit the prior knowledge that the abundance profiles of non-informative bacteria do not exhibit significant variation. This is achieved by adding a smoothness constraint on the recovered low rank matrix. The RegLRSD algorithm presents several advantages. First, RegLRSD improves the reproducibility performance because of the following traits: (i) RegLRSD incorporates prior knowledge in the detection process, which constrains the analysis. Consequently, this mitigates the conventional challenges associated with the HDLSS nature of metagenomic data. (ii) The multivariate nature of RegLRSD algorithm accounts for the complex interactions between the members of the bacterial community. This contrasts the univariate-based methods  that ignore such sophisticated relationships between bacteria. Second, the proposed matrix decomposition formulation is convex. This provides several benefits such as: (i) global optimality, (ii) efficient solvers, and (iii) flexibility to add convex constraints without affecting the convex structure of the problem. Third, unlike feature transformation-based algorithms, the output of RegLRSD is easily interpretable in the sense that it keeps the features in their original domain.This paper also sheds light into the design of an evaluation protocol which provides a fair and an accurate assessment of the efficiency of a biomarker detection algorithm. The absence of the “ground truth”  prevents the objective evaluation of the biomarker detection methods. Therefore, the assessment criteria and comparisons have to be conducted with great care to make sure that all the existing prior knowledge about the true markers is taken into account.D∈ℜp×n of bacterial abundance data, each line of D denotes the relative abundance of an OTU in all the n samples, and each column stands for the abundance values of all the p OTUs in one sample. In general, p≫n. Therefore, it represent a challenging high-dimensional small-sample size problem. The backbone of our approach is to capture the differentially and non-differentially abundant OTUs via a sparse matrix and low-rank matrix, respectively. In particular, most of the bacterial groups do not play any role in the considered biological system. Thus, these inappropriate OTUs are expected to exhibit high abundance levels that do not change significantly between two different phenotypes. Therefore, it makes perfect sense to model their abundance-level matrix as a low-rank matrix (represented by matrix L). Also, the abundance levels of the few key OTUs might present relevant changes between the two phenotypes. Such a condition will be captured by means of a sparse matrix . Mathematically, Consider the matrix Exploiting the low rank-sparse decomposition model of the bacterial abundance profiles , identifS∥0 denotes the l0-norm of the matrix S, which by definition is equal to the number of nonzero elements in S. Problem  by carrying out a convex optimization, referred to as the Principal Component Pursuit (PCP). This convex formulation is based on recent theories and results that show: (i) the l1 norm represents the closest convex approximation of the l0-norm, and minimizing l1-norm yields the sparsest solution to underdetermined linear systems [where ∥ Problem  is commo Problem , is highthors in , 26 poin systems , (ii) th systems . Mathemaλ represents a positive regularization factor that monitors the degree of sparseness and smoothness in S and L, respectively. Variable ∥L∥∗ stands for the nuclear norm of L and is equal to the sum of the singular values. Finally, the notation ∥S∥1 denotes the l1 norm of S, and it is defined as the summation of the absolute values of the matrix elements.where S and L, we extend the formulation in  is adopted as a measure of smoothness, which is defined as: In an attempt to enhance the estimation accuracy of ation in  by addinxj denotes the jth column of X, and F represents the first order difference operator defined as: where Thus, the RegLRSD algorithm aims to untie the optimization problem: ith row of L. One key advantage of this formulation is that that the optimization problem  with respect to S (matrix L is considered constant), and then it optimizes f with respect to L (matrix S being considered a fixed constant). In particular, it adopts the following updating steps: where This strategy utilizes the fact that the two sub-problems  and 8)  8) admitProblem  admits tshrinkage operator, expressed as: where τ≥0 denotes the threshold level. In the case of a matrix, the shrinkage operator will be applied onto each constituent element of the matrix. The problem in (and where oblem in  can be cL∥∗ and nuclear-norm regularized least-squares (LS) optimization problem which presents a closed-form solution [total variation denoising problem [The current formulation of the optimization problem in  is neithmentclasspt{minimamentclasspt{minimasolution . The sec problem , which p problem . In partmentclasspt{minimaith row of the auxiliary variable Y. To solve + denotes the positive part of x += max). In other words, Dτ(X) employs a soft-thresholding operation onto the singular values of X, shifting these towards zero. This is the reason why this transformation it is also referred to as the singular value shrinkage operator.where Considering Remark-1, problem  is recasY are updated separately according to the optimization: The rows of zi and li are the ith rows of Z and L, respectively. Problem . Then, the D=L+S). The stronger the variation in the abundance levels of OTUs, the larger the magnitude of the corresponding elements in the sparse matrix S. It is pertinent to mention that the strength of the variation of each OTU between the two phenotypes is determined by the absolute values of the non-zero entries in S rather than their exact values. This is because the elements of S could be either positive or negative based on the role  played by the microbes. Therefore, the score of the ith OTU is achieved by adding up the absolute values of the elements located on the ith line of S. Thus, the scoring vector sv is expressed as : The reasoning for employing the sparse matrix for extracting the potential biomarkers is that the abundance levels of informative OTUs can be considered to be a sparse perturbation matrix superposed over the low-rank matrix that models the abundance levels of the non-informative microbes , smoothness i.e.,∥S∥0), and fitness L∥∗+λ∥S∥0. The authors in [L and S can be recovered with high probability when α=1 and RegLRSD algorithm is equipped with four regularization parameters, lties in . In ordelties in , which ithors in  proved tρ, which is the single parameter that is associated with the ADMM method, the ADMM technique is known for its robustness to poor selection of its parameter. Specifically, the convergence of ADMM is guaranteed, under broad assumptions, for all positive values of its parameter [ρ=1. In addition, herein paper, we set β=0.1α.In what concerns the fitness penalty parameter arameter . Here, whttps://sites.google.com/a/tamu.edu/mustafa/software/reglrsd. This package is provided with a graphical interface to enable the user to set the algorithm parameters and to report the detected markers.The RegLRSD algorithm is carried out in MATLAB and exploits the original codes of the SFA algorithm  in ordedi) to determine the mean  of each class. The average vakue of the k′th class  to the class that presents a closer centroid. This reduces to the optimization: The second step assigns a test sample  . The reaThis dataset compares the fecal microbiota between 10 healthy dogs and 12 dogs diagnosed with IBD. The extracted DNA from fecal samples was sequenced by 454-pyrosequencing. OTUs were attributed by making sure at least 97% sequence similarity against the Greengenes reference database  using QuThree day pooled fecal samples were gathered from 18 healthy dogs and 7 dogs with EPI. Extracted DNA was sequenced by Illumina sequencer, and the generated sequences were analyzed using QIIME to obtain the final OTU table with at least 97% sequence similarity against the Greengenes reference database. The sequences can be accessed in the NCBI-SRA database under the accession number SRP091334.−/− x Rag2 −/− (UC) and 10 Rag2 −/− (control) mice was assessed using 16S data from fecal samples. The taxonomic relative abundance table is publicly available in the Supplementary Material of [This data set stands for the fecal microbiota of the mice model with UC and control mice. The description of the samples collection, processing and DNA extraction is described in . The micerial of .This section presents the comparison of RegLRSD algorithm with the latest existing algorithms over the three metagenomic investigations described in the Material and Methods Section. In particular, the RegLRSD algorithm is contrasted with LEFSe  and MetaThe competing algorithms were evaluated based on their classification and reproducibility performance. The essence of this evaluation relies on generating a high number of variations in the original dataset. Then, the evaluation metrics are computed by averaging the results obtained over all these different variations as shown Algorithm 2. The details of the evaluation protocol is discussed in the following two subsections.The reproducibility performance of a biomarker detection algorithm is empirically measured by generating different variations of the original dataset, and comparing the output of the algorithm based on these different variations. The reasoning behind this procedure is that a stable biomarker detection approach must provide alike outcomes in the presence of small variations in the data samples. This requirement is in line with the hopes of biologists that expect that changing the sample size by taking out or including a few samples must not alter dramatically the biomarkers detected by the algorithm.r∈. This random division is repeated K times, and the sub-index k represents the iteration number. Second, the biomarker detection algorithm is applied on each of the K training subsets. This results in K sets of potential biomarkers /2 pairs of the marker sets is measured by means of a similarity index. Fourth, the reproducibility performance of the algorithm (Cavg) is expressed as the mean of the all pairwise similarities, i.e., The evaluation methodology for estimating the reproducibility performance can be formalized as follows. First, divide the original dataset SI stands for the similarity index that measures the similarity between any two marker sets where dex (KI)  was adopwhere K subsamples for each selected marker. A perfect algorithm in the sense of stability of the ranked lists will have boxplots that are centered at the 45° line, which means that the algorithm perfectly preserves the order of the detected markers in all subsamples.In this paper, the stability performance was visualized by presenting three types of descriptive plots. The first plot shows the average KI over all pairwise comparisons. The second plot provides more details about the distribution of all the KI values by presenting their histogram. An ideal algorithm in terms of stability will have the Dirac-delta distribution at KI equal to 1. This means that the algorithm generates the same set of markers over all subsamples. Practically, the more concentrated the histogram is to the right side of the plot, the more stable is the algorithm. The third plot aims to depict the stability of the ranked microbial marker lists. This is achieved by ordering all the selected markers based on their ranks. Then, a boxplot is generated for the ranks obtained in all the TN and TP represent the number of correctly identified negative and positive subjects. Consider that FN and FP represent the number of false-predicted instances in the negative and positive classes, respectively. The accuracy, sensitivity and specificity measures are expressed as: Accuracy, sensitivity, and specificity are the three metrics that were used to measure the classification performance. The classification accuracy represents the fraction of the number of samples that were correctly predicted to the total number of samples. One major drawback of accuracy is that its value is dominated by the class with the majority of samples. Therefore, in case of imbalanced class distribution or when the forecast of the minority group is critical, accuracy may be misleading. Thus, class-specific measures  are needed to provide a more accurate picture about the classification performance. Sensitivity (specificity) is expressed as the contribution of the correct predictions in the positive (negative) class. Formally, let kth iteration, the classifier is trained by the data corresponding to the selected markers K times is to mitigate the over-optimistic results that are associated with the conventional cross-validation on small-sample studies [l1 norm, while the second version exploits the l2 norm. Therefore, herein paper, the first classifier is referred to as NCC-1, while the second one is denoted as NCC-2.The classification performance is measured empirically according to the evaluation protocol shown in Algorithm 2. At the  studies . In our A critical challenge for assessing the performnce of biomarker detection approaches is the lack of information about the true biomarkers. This hampers the objective assessment of the performance of competing biomarker selection algorithms. To overcome this challenge, evaluation criteria have to be properly developed to replicate comparisons as if the true markers were known. The evaluation criteria have to capture the features of the true biomarkers. The true biomarkers exhibit two properties. The first feature is the fact that the true biomarkers must allow differentiating different phenotypes. In general, this is assessed via the performance of a classifier designed based on the selected biomarkers. The second feature relies on the fact that true signatures appear not to be sensitive against variations in the training samples. This feature is evaluated via empirical assessment of the biomarker identification algorithm stability.only the classification performance as a measure of the effectiveness of a biomarker detection algorithm. In addition to ignoring the reproducibility performance, relying solely on the classification performance may be misleading for several reasons. First, the classification performance depends on factors other than the quality of the selected variables . In particular, the preprocessing steps and classifier model employed significantly impact the classification performance. Second, in the small sample size setups, the empirical estimation of classification accuracy may not reflect the true performance of a classifier.A common practice is to use Unfortunately, the existing metagenomic biomarker identification schemes have not yet considered the reproducibility performance in their assessments. This calls the utility of these methods under question. Similarly, assessing a biomarker detection algorithm based on its stability performance is delusive. For example, a trivial algorithm that returns the same features irrespective of the training samples will achieve a perfect stability performance. Thus, reproducibility needs to be assessed together with the classification performance.K=500) variations of the original dataset. Each subsample contains 80% of the samples in the original dataset . The classification and consistency performance were evaluated at different number of selected markers to provide further insights on the performance of the competing algorithms under varying sizes of the biomarker sets. The reported outcomes stand for the average over the 500 experiments.The classification and consistency metrics were used to measure the efficiency of the six biomarker detection algorithms in identifying potential markers. The consistency-classification evaluation protocol is presented in Algorithm 2. In our studies, a random subsampling without replacement is utilized to generate 500 subsamples . In particular, the preprocessing steps and classifier model employed significantly impact the classification performance. Second, in the small sample size setups, the empirical estimation of classification accuracy may not reflect the true performance of a classifier.A common practice is to use Surprisingly, the existing state-of-art metagenomic biomarker detection algorithms have not considered the reproducibility performance in their assessment. This calls the utility of these methods under question. Similarly, assessing a biomarker detection algorithm based on its stability performance is delusive. For example, a trivial algorithm that returns the same features irrespective of the training samples will achieve a perfect stability performance. Thus, reproducibility needs to be assessed together with the classification performance.K=500) variations of the original dataset. Each subsample contains 80% of the samples in the original dataset . The classification and consistency performance were evaluated at different number of selected markers to provide further insights on the performance of the competing algorithms under varying sizes of the biomarker sets. The reported results represent the average over the 500 experiments.The classification and consistency metrics were used to measure the efficiency of the six biomarker detection algorithms in identifying potential markers. The consistency-classification evaluation protocol is shown in Algorithm 2. In our experiments, a random subsampling without replacement is utilized to generate 500 subsamples /2=124750 comparisons; K=500) of the six algorithms for a changing number of biomarkers from the EPI dataset is illustrated in Fig. The reproducibility performance in terms of the average KI stability values over all the pairwise comparisons .The classification performance of the six algorithms in the presence of a changing number of biomarkers from the UC mice model data set is illustrated in Fig. The top 15 detected biomarkers by the RegLRSD algorithm are depicted in Fig. Recent advancements in metagenomic sequencing associated microbes with several health and disease states of the host. Identifying potential metagenomic markers is essential for understanding biological systems and designing possible therapies for diseases. Therefore, developing robust and stable biomarker detection algorithms is crucial in order to infer correct biological statements and translate these results into clinical practice. Herein paper, we developed the RegLRSD algorithm for biomarker detection. Apart from the conventional statistical and feature selection frameworks to tackle the problem of finding potential metagenomic biomarkers, RegLRSD formulates the biomarker detection as a matrix decomposition problem. In particular, RegLRSD models the abundance profiles of relevant and irrelevant microbes as sparse and low-rank matrices, respectively. This renders identifying potential biomarkers as the problem of decomposing the bacterial abundance data matrix into a sparse matrix and a low-rank matrix.To enhance the accuracy of estimating the low-rank matrix and the sparse matrix, RegLRSD constrains the low rank matrix to be smooth in order to integrate the prior knowledge that the abundance profiles of irrelevant bacteria do not exhibit strong variation between different phenotypes in the biomarker detection process. Then we developed an efficient solution for this decomposition problem by exploting the alternating direction method of multipliers. In addition to the computationally efficient solution for RegLRSD, a major advantage of RegLRSD is the convex formulation of the biomarker detection problem. This convex formulation enables adding convex constraints that reflect our prior knowledge about the biological system under study. These additional constraints help in designing better algorithms that are more accurate and provide more consistent biological findings. The improved performance of RegLRSD over the conventional RPCA algorithm  demonstrates the efficiency of incorporating prior knowledge in the design of a biomarker detection algorithm.In addition to the development of a novel algorithm for identifying metagenomic markers , this paper addressed an important feature of the metagenomic biomarker discovery algorithms. This feature is the ability of biomarker detection algorithms to generate reproducible results. This is crucial to translate the outcome of these algorithms into practical applications. Surprisingly, the stability/reproducibility performance was not addressed by the existing metagenomic biomarker identification algorithms. Our simulation results demonstrate that the existing methods for metagenomic biomarker discovery present poor reproducibility performance. In particular, the spread of the histograms of LEFSe, MetaStats, MetaBoot and entropy-based algorithm over a wide range of KI values indicates a serious inconsistency problem that puts the outcomes of these algorithms under question.Comprehensive comparisons with the latest biomarker detection approaches were conducted. In particular, RegLRSD was contrasted with two statistical-based approaches , two machine learning-based algorithms (MetaBoot and entropy) and a reduced from of RegLRSD in which the smoothness constraint is not considered . The competing algorithms were tested against three realistic metagenomic datasets. The first and second datasets pertain to healthy dogs and dogs diagnosed with EPI and IBD, respectively. The third dataset refers to a mouse model of UC. These approaches were assessed in terms of classification accuracy and reproducibility performance. The simulation results show that the detected markers by RegLRSD enable discriminating metagenomic samples belonging to different phenotypes with a quite high accuracy. Moreover, RegLRSD exhibits superior consistency performance when compared to other algorithms. This renders the RegLRSD algorithm as a robust and reliable tool to identify potential metagenomic markers that may characterize the difference between samples belonging to different phenotypes.The results presented in this paper demonstrate that the two matrix decomposition-based algorithms  are successful in providing high reproducibility and classification accuracy performance compared to the conventional statistical and machine learning-based algorithms. This validates the idea of modeling the bacterial abundance data matrix as the superposition of a low-rank matrix representing the uninformative microbes and a sparse matrix containing the abundances of informative microbes. Moreover, the improvement in the performance of RegLRSD compared to RPCA demonstrates (i) the validity of our assumption that the abundance profiles of irrelevant bacteria are smooth, and (ii) incorporating prior knowledge in the design of a biomarker detection algorithm may lead to more robust results.https://sites.google.com/a/tamu.edu/mustafa/software/reglrsd.Due to the necessity of developing user-friendly tools that enable the researchers to analyze metagenomic data, RegLRSD is implemented as a standalone executable software package and is made available at


In this paper, DSC curves at different heating rates were measured by DSC, the characteristic curing temperature was determined, and the optimum curing conditions were obtained. The KAS method, Friedman method and FWO method were used to analyze the DSC curves respectively. The kinetic parameters and the reaction mechanism function of the curing system were obtained, and the results of different analytical methods were compared and analyzed. Result from fitting and verification of the curing kinetic model for the curing system of the nano-phase modified epoxy resin further demonstrates that the nano-particle could play a catalytic role in the curing reaction of the epoxy resin and could reduce the apparent activation energy of the system, thus it is considered as a breakthrough in the field of resin research. The raw material of bisphenol A epoxy resin is easy to get, with high cost performance. As a kind of glycidyl ether type epoxy resin with the largest output, bisphenol A epoxy resin approximately takes up 90% of total output of epoxy resin in China and accounts for about 80% of total output of epoxy resin in the world. As a result, bisphenol A epoxy resin is known as universal epoxy resin. However, epoxy resin prepolymers which will not be cured under normal temperature and general heating conditions cannot be applied directly. Usually, curing agent or promoter can be introduced and the performance of epoxy resin prepolymers can be improved through the optimization of thermocuring process to turn epoxy resin prepolymers into thermosetting materials with real use value. However, the internal temperature of resin undergoes complicated changes in the curing process, which will usually lead to the phenomena including a large gradient in local temperature and concentration of thermal stress and even damage the internal structure of cured substance3. When alkyl etherification reaction is caused by the insufficient dosage of curing agent and the excess of epoxy group, the glass transition temperature (Tg) of cured substance may be too low, which will have an influence on the final use performance. If curing agent is excessive, residual curing agent may have an impact on curing system4. Therefore, it is very important to look for the appropriate equivalence ratio of epoxy resin/curing agent and design reasonable thermocuring programs. Amine curing agent is the most important category of epoxy resin curing agents, whose performance determines the use performance of epoxy resin to a large extent. Aliphatic amine curing agent is a type of widely-used curing agents. However, these cured substances are far less than aromatic curing agents due to their generally poor heat resistance5.With the advantage of high bonding strength, good dimensional stability and chemical resistance, high mechanical strength, excellent electrical insulation and strong radiation resistance, epoxy resin is widely used as coating, adhesive, electronic electrical material, civil engineering material, engineering plastics, composite material and so on6. Epoxy resin crosslinking curing process is generally divided into three types: free radical polymerization, condensation reaction and addition reaction types. The common feature is that it releases the chemical reaction heat during the curing reaction. Epoxy resin undergoes complex physical and chemical reactions and complex chemical reaction kinetics processes during crosslinking and curing9. It is very important to study the mechanism of curing process, in order to reveal the mechanism of curing reaction, and control the curing reaction process. It is also important to optimize the curing process parameters. There are many ways to study the curing process, among which the thermal analysis method is the major one.Epoxy resin is an epoxy oligomer, which can form a three-dimensional network of thermosetting material when it reacts with the curing agent14. Thermal analysis kinetics is to use the thermal analysis technology to study the physical changes and chemical reactions, by means of a certain mathematical approach to obtain the corresponding reaction of the kinetic parameters and reaction mechanism21. There are two methods of thermal analysis kinetics: isothermal and non-isothermal methods. The non-isothermal method has many advantages over the conventional isothermal method22. So it gradually becomes the main method of thermal analysis kinetics. Non-isothermal method is divided into the single scanning rate non-isothermal method and the multiple scanning rate non-isothermal method24.Thermal analysis method is used to study the solid process through the thermal tracking detection of polymer materials in chemical reaction26. According to the different mathematical methods, it can be divided into two categories of differential and integral methods. At present, in the method of research dynamics, the differential method is mainly represented by Kissinger-Akahira-Sunose (KAS) method and Friedman method, and the integral method is mainly represented by Flynn-Wall-Ozawa (FWO) method30.Multiple scanning rate non-isothermal method refers to using different heating rates measured under a number of dynamic curves to carry out the method of dynamic analysis31. Friedman method and FWO method are used in a same conversion of several kinetic curves of the data. They are is also called as the Isoconversional method, which can obtain more reliable values of the activation energy E in the premise of not involving the kinetic model function eliminating the effects of model function in calculating Arrhenins parameters. So it is also known as a model free method. As the results show that the multiple scanning rate method and the equal conversion method can describe the complex solid - state reaction kinetics more effectively.The KAS method is based on the assumption that the maximum rate of the curing reaction occurs at the peak temperature of the curing reaction exothermic peak, so the KAS method is also called as the maximum rate method32. The advantages of this method include use of less sample, convenient operation and strong ability of data analysis, particularly suitable for thermosetting resin curing reaction kinetics of the study.The epoxy resin kinetic model parameters are typically obtained by differential scanning calorimetry (DSC)The 21st century features robust development of macromolecular materials, among which the epoxy resin is the pilot in the development and application in this area. Epoxy resin is widely used in electrics, semiconductor electronics, special coating aeronautics, astronautics, etc., owing to its excellent properties. The curing kinetics analysis on epoxy resin is helpful to better understand the characteristics of its system, the curing conditions and the impact imposed on the curing activities by the dimensions of curing molds. It can also help to establish a quantitative relation between the curing conditions, epoxy resin, as well as the chemical structure of curing agents and the properties of curing objects to reduce the experiments in order to optimize the curing process.As an amorphous viscous liquid, epoxy resin is plastic when heated, without clear melting point, becoming soft when heated, turning sticky when gradually melted, insoluble in water, and will not harden by itself. Therefore, it almost has no value to use independently. It must first react with curing agents to create a polymer with a three-dimensional structure which does not melt or dissolve, and then it can be usable. When a certain amount of curing agents is added, it gradually hardens and turns into chemical materials with distinct properties. Therefore, curing agents must be added to react under certain conditions to turn into a cross-linked network structure in order to create formula resin. In this way, the epoxy material can display excellent properties and be truly usable. The common amine curing agents are: ethylenediamine, diethylene polyamine, and polyethylene polyamine, etc.Ammonium hexafluoroantimonate is a kind of Cationic curing agent by heated, which could trigger low temperature and curing speed, and dissolve in epoxy resin. Curing products have better adhesion and corrosion resistance, anti-aging properties.The curing mechanism is shown in Fig. Firstly, epoxygroups in the epoxy group react with ammonium hexafluoroantimonate to capture its active hydrogen, to generate oxygenium ions, with hydroxyl groups of oxygen ions as the initiator, and epoxy groups continue to react, to open the way for chain ring polymerization of curing epoxy resin. As long as the epoxy repair agent is in contact with the curing agent, the curing reaction can be automatically carried out. Because the curing agent can be well dissolved in the epoxy resin, it could ensure that micro-cracks derive from self-healing, and achieve a high polymerization rate and polymerization degree.Figure The curing reaction exothermic peak of the epoxy resin and the nano rubber modified epoxy resin system are related to the curing temperature and time in different heating rates, as shown in Figs dH/dt of the system becomes larger, which means the increase of the thermal effect per unit time and the thermal inertia. The greater the temperature difference is, the higher the temperature of the curing reaction exothermic is. After the addition of nano-particles, the exothermic peak can be seen to be sharpened  equation is as follows:E and A of each curing system are obtained from the slope and intercept of the straight line in the figure. The results are shown in Table E and A can be seen by KAS method. The results show that the E and A of the curing system are obviously decreased after the addition of the nanoparticles, which indicates that the reaction system is more rapid after adding the nanoparticles.The Friedman equation is as follows:The DSC curve in Figs mentclasspt{minimaentclass1pt{minimaIt can be seen from Figs The E and A data obtained by Friedman analysis in Table Flynn-Wall-Ozawa (FWO) equation is as follows:According to the FWO equation, the DSC curve of the graph is analyzed, and the linear regression curves of Figs For each curing system, using the slope and intercept of the regression curve at each conversion rate, the corresponding conversion rates E and A were obtained. The results are shown in Figs As it can be seen from Table The Eand A values obtained by the FWO method in Table Therefore, FWO method is used to analyze the system of E. The addition of organic nano particles, the apparent activation energy and pre-exponential factor decreases significantly, which indicated that nanoparticles to the same catalytic have an effect on curing up.p, E, R is the same as above, the reaction series is n. The above formula can be integrated,The reaction series of the curing reaction can be obtained by the Crane equation.It can be seen that the apparent activation energy obtained by the KAS method, the Friedman method and the FWO method are not all integers, indicating that the curing reaction is a complex reaction and the results of the three methods are also correct.The commonly used kinetic equation for nonhomogeneous systems under non-isothermal conditions is shown as follows:The above equation can be transformed into a linear equation:Based on the function expression of different reaction models f(α), the computer being used to analyze the data fits with the commonly used dynamic mechanism function to obtain the most suitable curing kinetic model.jk is the measured value, Yjk is the regression value, S is the number of measurements under different conditions, NS is the number of data measured at one time.2 is the degree of freedom of model 2 (reference model). The Fexp value is compared with Five different reaction models in the table were used to fit the previous data using the multivariate least squares regression method. The correlation coefficient (r) is defined as follows:The experimental data were fitted and the results are shown in Table Comparing the results of 5 commonly used dynamic mechanism function fitting, we can see that the curing reaction of epoxy resin modified by nano phase belongs to the PT kinetic mechanism function.Comparing on fitting curves and test curves, the result is shown in Figs From the chart it can clearly seen that the curing system of DSC curve to simulate the PT kinetic mechanism functions coincide with test results of DSC. In the meantime, reaction mechanism corresponds to the self catalytic reaction shows that nano modified epoxy resin and six hexafluoroantimonate ammonium curing reaction proceeds in accordance with the autocatalytic reaction and it is consistent with the previous reaction mechanism.The curing curves of the two epoxy resin systems show that the peak of heating becomes steeper with the heating rate increasing, and the curing time is shorter. The curing characteristic temperature of the system moves to the high temperature.After the addition of nanoparticles, the heat release was reduced and the time was shortened, indicating that the nanoparticles played a catalytic role in the reaction.Three kinds of analytical methods were used to study the curing kinetics of the two curing systems, and the kinetic apparent activation energy E and the pre - exponential factor A were obtained.The results show that the apparent activation energy and pre - exponential factors of the system are greatly reduced after the addition of the nanoparticles, which indicates that the nanoparticles can promote the curing reaction of the system and reduce the apparent activation energy of the system.The kinetic model of nano-phase modified epoxy resin was fitted by multiple linear analysis method and different reaction mechanism. The results showed that the curing reaction mechanism was autocatalytic reaction.


An irrigated agricultural landscape experiencing groundwater overdraft generates economic returns and a suite of ecosystem services . Alternative land cover choices indicate tradeoffs among the value of ecosystem services created and the economic returns. These tradeoffs are explored using efficiency frontiers that determine the least value in ecosystem services that must be given up to generate additional economic returns. Agricultural producers may switch to irrigation with surface water using on-farm reservoirs and tail water recovery systems in response to groundwater overdraft, and this has consequences for the bundle of ecosystem service values and economic returns achievable from the landscape. Planning that accounts for both ecosystem service value and economic returns can achieve more value for society, as does the adoption of reservoirs though lowering the costs of irrigation, increasing groundwater levels, and reducing fuel combustion and associated GHG emissions from groundwater pumping. Sensitivity analyses of per unit value of ecosystem services, crop prices, and the groundwater and water purification model parameters indicate tradeoff among ecosystems service values, such as the use of a high-end social cost of carbon ultimately lowers groundwater supply and water purification value by more than 15%. In response to groundwater overdraft, increases in groundwater pumping costs, and the degradation of surface water quality from agricultural runoff, irrigation may shift from well pumping toward surface water from on-farm reservoirs with tail-water recovery systems that capture agricultural runoff. The economic and institutional aspects of conjunctive water management are well studied , 2, but The conjunctive water management system we examine includes on-farm reservoirs to store abundant surface outside the irrigation season and tail-water recovery systems that brings the runoff leaving the field to the reservoir. The use of the surface water conserves groundwater and the collection of tail-water for reuse on the farm can improve surface water quality . The groA tradeoff in ecosystem service value foregone to achieve greater economic return is observed by the slope of the efficiency frontier. The landscape that is societally optimal is where the loss of a dollar of ecosystem service value is exactly balanced by a dollar gain in economic returns. Elsewhere on the frontier, social value can be increased by moving along the frontier toward the objective that gives more value than is given up. Building on-farm reservoirs with tail-water recovery may increase the efficiency of the landscape at providing both economic and ecosystem service value, but the extent that the frontier shifts outward is the empirical focus of this study.Irrigation in the Lower Mississippi River Basin in Arkansas  comes principally from groundwater within the Mississippi River Valley Alluvial Aquifer. The overdraft of the aquifer means that by 2050 agricultural demand for groundwater will exceed available supplies by 7 million acre-feet per year . We optiThe challenge is to create ecosystem service models that accurately represent the value of the ecosystem services associated with the crop and irrigation decisions in sufficient scientific detail while also being able to solve an optimization model to create the efficiency frontiers. We model changes in groundwater supply by tracking the aquifer’s saturated thickness in response to well pumping, and this depends principally on the hydro-conductivity of the groundwater flow and the proximity to nearby wells whose pumping alters the groundwater flow. The precipitation, evapotranspiration, the slope of the land, and the tillage and irrigation practices affect soil and nutrient runoff that can reduce the quality of surface waterbodies. The relationship between the land cover and pollutant loading of the surface water depends on a digital elevation model that routes water downhill to the streams. Farm tillage practices, soil type, and fuel combustion from irrigation pumping all influence the release of GHGs from land cover of the agricultural landscape.Much of the prior work that looks at the tradeoffs among multiple environmental objectives and economic returns does not use efficiency frontiers , 7, 8, 9However, a growing strain of literature is using efficiency frontiers to analyze the optimal tradeoffs among different ecosystem services or between ecosystem services and economic returns. Nelson et al.  use effiThis section describes the model components for creating the efficiency frontiers that examine the tradeoffs among economic returns and ecosystem services. The land cover is the basis for computing the economic returns and ecosystem services from the agricultural landscape. The economic model uses the land cover and irrigation decisions of the agricultural producer to maximize farm net returns. The ecosystem service model uses the land cover and irrigation decisions to calculate the water supply value, water purification value, and the GHG reduction value. We conclude the methods section with a description of how the economic and ecosystem service models are used to create the efficiency frontiers, the sensitivity analyses applied to the model parameters for calibration, and the conservation policies evaluated.T is chosen for a single generation of farmers to observe how depletion of the aquifer influences production decision, and a grid of m cells (sites) represents spatial differences.The land cover of the farm landscape includes crops, reservoirs, and conserved land set aside through a rental program of the government. The chosen crops generate economic returns, but irrigation depletes groundwater. Also, agricultural runoff pollutes surface water, and farm production activities release GHGs. The landscape is spatially heterogeneous due to differences in long term investment in farm practices, soil types, and access to water resources. A thirty-year time horizon n possible land cover types j at the end of period t as denoted by Lijt for site i that include each of the crops, reservoirs that have tail-water recovery, and the US Department of Agriculture’s Conservation Reserve Program (CRP). At the end of each annual period t, we assume any land cover j can become another land cover or on-farm reservoirs with tail-water recovery, except for CRP which cannot transition to another cover until a ten-year contract period has elapsed. A profit maximizing farmers may switch land out of irrigated crops into non-irrigated crops with declining groundwater availability at the end of each period.The major crops include flood irrigated rice, and furrow irrigated soybean, corn, and cotton, non-irrigated sorghum and soybean, and double cropped irrigated soybean with winter wheat. Arkansas has average annual precipitation amounts ranging from approximately 50 to 57 inches per year, and has often been considered an area rich in water resources . Howeveri at any time tThe initial land availability equals the sum of the land covers chosen for site The net present value of the agricultural production over all time periods and the entire landscape is the economic objective. We describe the irrigation components of the economic model first followed by the economic objective that the agricultural producers optimize.j receives to supplement precipitation, wdj, is the demand for irrigation in acre-feet. The groundwater stored in the aquifer beneath site i at the end of the period t is AQit. The water that comes from the on-farm reservoirs is RWit, and the water from well pumping is GWit. There is recharge of the groundwater, nri, that occurs naturally from precipitation, streams, and underlying aquifers each period.The average annual irrigation that crop LiRt, as the acres in reservoirs at time t, and the total acreage at site i, i and only the rainfall fills the reservoir, then the low-end acre-feet of water that fills each reservoir acre is ωmin. If the reservoir is less than the size of the site, then recovery of the runoff and rainfall fills the reservoir to a high-end capacity in acre-feet per reservoir acre of (ωmax + ωmin). Reservoirs are likely placed on land that is relatively less productive for agriculture. Through the model optimization, the least profitable land would be replaced by reservoirs, but the effect of soil differences within sites on the crops grown is not modeled. Variations in crop yield across sites mean that the sites with worse soil and lower yields will have more reservoirs. We do not account for within growing season variability in evaporation, leakage, and the timing of rainfall, which could influence ωmax and ωmin for the reservoir.k and out of site i when an acre-foot is pumped from a well at site k is pik, which depends on the distance and the lateral speed of underground water movement based on the soil profiles observed between sites. Jenkins [i in response to a nearby pump at site k as the hydraulic diffusivity of the aquifer divided by the square of the shortest distance between the pumped well and the nearby site. The groundwater flow out of site i into site k divided by the sum of all groundwater flow out of site i is the how the proportion pik is calculated. This means the groundwater that leaves site i is The intensity of well pumping across the landscape influences aquifer depletion over space. The proportion of the underground flow into the aquifer at site  Jenkins  quantifiGCit, depends on the cost to lift an acre-foot of water by one foot, cp, and the initial depth to the groundwater, dpi. The depletion of the aquifer volume, (AQi0 − AQit), divided by the area of the site, ccThe water used for irrigation must be less than the water available from reservoirs and wells Eqs caj and the price per conventional unit of the crop is prj are constant in real terms. We assume no productivity growth trend for the constant yield of crop j per acre at site i, yij. Excluding the costs of irrigation, the net value for crop j is then prjyij—caj per acre. The CRP payment per acre to the landowner, prcrpyicrp, with yield normalized to one and price is the payment per acre, less the cost to establish and maintain an acre of CRP (cacrp) is net value per acre of CRP. The reservoir pumping cost per acre-foot is crw, and the per acre capital and maintenance cost of a reservoir each period is cr. We make values over time comparable in monetary terms using the real discount factor, δt.The cost to produce an acre of the crop excluding the irrigations costs T by changing the amount of land in each crop or CRP, the reservoir water use, and groundwater use, namely Lijt, RWit, and GWit. The choice of the thirty-year fixed time horizon is chosen to represent the planning for a single generation of agricultural producers, and the time horizon does influence the results such that a shorter horizon leads to faster annual depletion of the aquifer and a longer horizon leads to slower annual depletion of the aquifer. The initial condition of the state variables and the non-negativity constraints on land, water use, and the aquifer are shown in Eqs tion Eqs . The croWe track changes in the physical ecosystem services over time, and the social value of these ecosystem services determine the monetary value of the changes in ecosystem services to calculate the net present value of ecosystem services from the landscape. The description of the model for calculating greenhouse gases reduction value is first followed by a description of the model components for the water purification value and the groundwater supply value.j (Ej). Pumping ground and reservoir water releases fuel combustion emissions, and the range of irrigation emissions is in Figure A.1 in σg that identifies the carbon emitted from fuel combustion to lift an acre-foot of water one foot and multiplied by the acre-feet of groundwater pumped indicate the emissions from groundwater pumping at site i, EGit. The acre-feet of reservoir water pumped multiplied by a conversion factor σr for the carbon emitted from fuel combustion to pump an acre-foot of water into a reservoir and back out to the field is the emissions from pumping reservoir water at site i, ERit. The total carbon emissions for time t at site i (Eit) is shown in GHG emissions per acre of vegetation on a land cover are associated with the production of crops and CRP for the major production practices of the Arkansas Delta based on a life cycle assessment (LCA) up to the farm gate . Fuel usAGBij) and belowground biomass (BGBij) sequester carbon, with the details of this in i determines the soil factor, ξi, which is the fraction of carbon lost to respiration due to soil related microbial activity. Porous soil (i.e. sandy) has more intense wetting and drying cycles, and this encourages microbial activity and respiration compared to finer textured soils (i.e. clay). Sit, for time t at site i asAboveground biomass   water puPijk0 are used to calculate how the pollutant loading to the mouth of the watershed change in response to the land cover transitions. We assume pollutant exports from site i are associated only with the land cover changes at site i but not with land cover transitions at surrounding sites. Assuming no spatial interactions of land cover transitions with surrounding sites is necessary to have the optimization problem to solve. These spatial interactions may have potentially large second order effects that are worth further investigation but are outside the scope of the paper here. Tail-water recovery systems have the ability to capture runoff and prevent this from reaching natural streams, but the slope at site i affects the effectiveness of the system. The tail-water recovery system effectiveness, 0 ≤ θi ≤ 1, is greater if site i is flatter [The values for  flatter .k reaching the mouth of a watershed from each site i at time t (EXikt) asPijk0Lijt is the export without reservoirs of the pollutant k to a stream from site i and land cover j. A site with reservoirs has a value less than one for LiR_t, and this reduces the export of pollutants to streams.wtpqb) depends on the baseline water quality and median household income of the basin b. The WTP values per household are prorated to the percent reduction in the pollutant k loading from all sites i in basin b, hhb) by the prorated WTP per household for pollutant k is the present value of the surface water purification, Vw, shown as The willingness to pay (WTP) per household for a water purification improvement .Three sensitivity analyses examine the responsiveness of the economic returns optimization to the economic and ecosystem parameters. These include looking at low- and high-end i) social values for the GHGs, groundwater supply, and water purification, ii) prices for rice, soybeans, and corn, and iii) the hydro-conductivity of the aquifer and the nutrient loadings from all the crops. The sensitivity analyses involve maximizing economic returns to see how crops and ecosystem services respond to the parameter changes while maintaining total ecosystem service value at a particular level on the efficiency frontier without reservoirs.Conservation policies are meant to align profit making decisions with the provision of ecosystem services. We compare the model of the economic returns objective with reservoirs and no conservation policy to the conservation policies that include 65% cost-share on reservoir construction costs based on the rate from Natural Resource Conservation Service’s Agricultural Water Enhancement Program , tax on The policies for the cost-share on reservoir costs and groundwater taxes result in transfers between the government and producers while the carbon credits policy cause transfers among producers. All the policies make the economic returns less transfers from the government fall, and this is an economic cost to society because the policy directs production decisions away from maximum economic returns. The economic cost per dollar of ecosystem service value gained is calculated as the difference in economic returns without and with the policy and dividing this by the difference in total ecosystem service value with and without the policy.The outer boudary of the study area consists of three eight-digit hydrologic unit code watersheds in the Arkansas Delta region with critical groundwater areas and non-point source pollution priorities . These wi. Precipitation and the flow from streams and the underlying Sparta aquifer influences the natural recharge (nri) [Table A.1 in ge (nri) .pik) is based on the hydraulic diffusivity of the aquifer divided by the square of the shortest distance between the pumped well and the nearby site [Well pumping reduces the aquifer below the pumped well and for the sites that surround the well. In response to well pumping, some of the aquifer flows from the surrounding sites into the site with the pumped well. The lateral flow of water that determines the spatial weight  an acre reservoir will hold comes from the tail-water recovery system collecting rainfall alone to fill a reservoir by 1.4 acre-feet of water [θi) varies according to the slope of each site i [The minimum volume of water . As the water travels over each site, the site either subtracts or adds depending on the land cover, to the quantity of the nutrient (Table A.3 in 2). The low- and high-end social value of carbon abatement is $66 and $209 per ton carbon based on the 33rd and 67th percentiles for the fitted distribution and 1% pure rate of time preference.Using the production estimates from crop enterprise budgets , we tracTwo efficiency frontiers  examine Point A in The availability of reservoirs does not affect the maximum ecosystem services because a landscape entirely in CRP provides the greatest ecosystem service value. By moving from point I to point L, economic returns increase by 101% because irrigated crops be grown at a lower cost because of less well pumping see . CompariThe prices farmers receive for a crop influence which crops are grown to maximize economic returns when the total ecosystem service value cannot fall below the value at Point D . High-enThe crops chosen to maximize the economic returns for the required total value of ecosystem services at Point D depend on the per unit social value of each ecosystem service . A high-The high-end value for the groundwater buffer value makes the groundwater supply value rise, but the water purification and greenhouse gas value fall to maintain the total ecosystem service value at Point D. The crop change associated with the new mix of ecosystem services include an increase in the non-irrigated crops and CRP, and a fall in the irrigated corn, soybeans, and cotton. The economic returns rise by 2% because a high-end buffer value for groundwater allows the total ecosystem service value requirement to be achieved more easily. When the low-end buffer value of groundwater is used, there is less non-irrigated crop, less CRP, and more irrigated crops, and the groundwater supply value declines and the greenhouse gas value rises. The low- and high-end WTP for pollutant loading reduction has minimal influence on the crops grown and the associated ecosystem service values. Less corn and more non-irrigated crop is grown with the high-end WTP value for water purification, and this slightly raises water purification value and slightly lowers greenhouse gas value. The economic returns are larger with the high-end value of the WTP, but this is an increase of less than 1%.The hydro-conductivity of the aquifer measured at the high-end with greater lateral flow in the aquifer and shallower cones of depression or at the low-end with lower lateral flow in the aquifer and deeper cones of depression can also influence the crops grown to maximize economic returns . The aquThe nutrient loadings to surface water lower the value of water purification, and this affects the crops grown because if crops generate greater nutrient loadings then less polluting crops must be grown to maintain the required total value of ecosystem services at Point D. High-end nutrient loading from the crops cause there to be less of the heavily polluting corn, cotton, and non-irrigated crops and more of the lightly polluting soybeans and CRP. Even though heavily polluting crops decline on the landscape, the high-end nutrient loading makes the water purification value fall, and the water supply and the greenhouse gas values rise. Economic returns fall with the high-end nutrient loading because less high value crops can be grown to maintain the required total value of ecosystem services. In the low-end nutrient loading scenario, more high economic value crops that are heavily polluting can be grown, and this means more corn, cotton, and non-irrigated crops. When all the crops are less polluting, the water purification value rises in spite of a shift toward the more heavily polluting crops, but the increases in irrigation intensive crops and the fall in CRP makes the water supply value and the GHG value fall.A total maximum daily load (TMDL) improves surface water quality by increasing land in rice, CRP, and reservoirs, and the water supply and GHG reduction value also increases. The increase in CRP land at the expense of corn makes the economic returns fall. The value to society from the TMDL rises only 4%, and the economic cost per ecosystem dollar gained is the highest of the policies. A carbon credit policy decreases rice and irrigated soybean, and the increases in reservoirs and sorghum reduce the GHG emissions from irrigation related fuel combustion. The carbon policy has a higher economic cost per ecosystem dollar gained than the cost-share on reservoir construction or the tax on groundwater.The use of efficiency frontiers to examine the tradeoff of economic returns and the value of ecosystem services indicate that a compromise among objectives generates more social value than directing the landscape exclusively to one objective. The social value of a landscape that incorporates economic and ecosystem service value is 30% greater than a landscape at the endpoint of a frontier where only ecosystem service or only economic value is taken into account. We find this compromise for the Arkansas Delta partly because corn generates strong economic returns and effectively sequesters GHG while using less irrigation water than rice. A higher social price for water purification makes polluting corn less effective at bridging economic and ecosystem objectives. Also many crops on the landscape, such as non-irrigated sorghum or irrigated soybeans, provide moderate economic returns without significantly harming ecosystem services.Sensitivity analyses of the economic and ecosystem service model parameters reveal tradeoffs that occur among the land cover and ecosystem service values when the total value of ecosystem services is held at a constant level. The high-end social cost of carbon redirects the landscape toward more corn and CRP and less cotton, and this decreases groundwater supply value by 15% and water purification value by 20%. The high-end buffer value for groundwater or high-end WTP for a reduction in nutrient loadings directs the landscape toward more CRP and non-irrigated crops, and this lowers the GHG value by 1%. Higher prices for rice, soybeans, and corn decrease the groundwater supply value because all are irrigated crops while the GHG value increases for soybeans and corn by 1% and the water purification value increases for rice by 9%. The low-end hydro-conductivity reduces irrigated crops because the cones of depression deepen, and the greater groundwater conservation raises the groundwater supply value by 7.4% and the decline in corn lowers the GHG value. The high-end nutrient loadings to surface water decrease the corn and cotton and increase the CRP and soybeans, and the water purification value declines by 10%.Reservoirs allow irrigated crop production to expand and use less groundwater, but the expansion of irrigated crops increase GHG emissions and surface water pollution. Reservoirs support a landscape with a higher value to society, up to 10% greater economic returns for a given level of all ecosystem services in some cases, but tradeoffs among ecosystem services mean that not necessarily all ecosystem services improve even when valued at their appropriate social value. Conservation policies can shift the landscape away from the maximum economic returns toward greater ecosystem service value, but this is done at an economic cost to society. Policies targeting groundwater conservation, either with a cost-share on reservoir construction costs or a tax on groundwater, increase the value of the ecosystem services at the least economic cost. The groundwater conservation policies are the most effective because this discourages GHG emissions from well pumping and discourages the production of the potent GHG emitting rice.The methodology developed here is designed for use in an optimization model capable of creating efficiency frontiers for the use in long term planning by natural resource agencies when an agricultural landscape experiences groundwater overdraft. The usefulness of the efficiency frontier is that tradeoffs reflect the least that must be given up in ecosystem service value to obtain additional economic return. Given scarce budgets to achieve environmental and economic objectives, finding ways to achieve the most economic return with the least environmental degradation is worthwhile. However, assumptions of the economic and environmental components of the model are necessary for optimization to successfully occur and create efficiency frontiers. Significant assumptions of the economic model include constant prices for crops and ecosystem services and constant production costs for crops over time, although the consequence of these assumptions are explored with the sensitivity analyses. Significant assumptions on the ecosystem service model include the absence of interaction between surface water and groundwater except for a constant level of natural recharge to the aquifer and a lack of spatial interaction among the land covers chosen and the resulting surface water quality. As computing power and the solvers for optimization improve, these model additions can be included in analyses that use efficiency frontiers.There is the potential for multiple lines of further inquiry for this research. The value of groundwater extends beyond its ability to augment surface water supplies and reduce the variability of the total water supply. Other values that relate to maintaining groundwater supply are the avoided damages from subsidence, prevention of saltwater intrusion, dilution of groundwater pollutants, and the support of terrestrial ecosystems through groundwater exfiltration and contributions to transpiration. Feedbacks between water conservation practice adoption such as reservoir construction and land prices is an issue not considered here, which could then drive cropping decisions on other agricultural land. Land market feedbacks and water conservation practice adoption factor into the discussion of policies to reduce groundwater overdraft , 41. WhiS1 Supporting Information(DOCX)Click here for additional data file.


Prevotella sp. and human collagen epitopes with high similarities were positive for dot immunobinding assay. With this result it can be suggested that the mimicry phenomena can occur on periodontal disease.Periodontal disease afflicts 20% of world population. This process usually occurs in the form of being lethargic and chronic, and consequently this disease is known as chronic process. All chronic diseases constantly cause activation of the immune system, and therefore the presentation of microbial peptides which are presented to lymphocytes by professional antigen presenting cells can present microbial peptides very similar to important structures of human economy causing autoimmune diseases, process known as molecular mimicry. Thus, the aim of this study was to verify the presence of molecular mimicry phenomenon between periodontopathogens and human proteins. Blasting microbes of Socransky periodontal complexes against human collagen were performed and then the proteins with similarities were modelled and were screened in the MHI binding virtual methods. The epitopes selected were produced and plasma of chronic periodontal volunteers was obtained and a dot immunobinding assay was performed. Hypothetical protein of Porphyromonas gingivalis, Treponema denticola, and Bacteroides forsythia, belonging to the red complex [Since the publication of the subgingival plaque's microbial complexes by Socransky, there has been more knowledge about the microorganisms which are linked directly to the evolution of the periodontal disease; and it has been possible to determine new therapies for its treatment . Neverth complex .These strict anaerobic bacteria have many factors of virulence that activate the immune system in such a way that the immune response causes the destruction of dental support tissues. Another interesting revision of Ximenes describes the complexes of the supragingival microflora, which demonstrates that there are few differences in comparison to the subgingival complexes .The immune response against these microbial complexes has only one similarity: inflammation. As it is known, the subgingival plaque produces inflammation of the gingiva, but without the destruction of the dental support tissue. It is so that once eliminated the cause (that is to say) by removing the dental plaque, this inflammation disappears; but if the inflammation continues due to the dental plaque accumulation, it can cause the activation of the immune system both innate and adaptive, causing the resorption of the dental support. If this process is not stopped by periodontal therapies, it will eventually cause the exfoliation of the dental pieces .α, IL-1β, and IL-6 once secreted will be the responsible in charge of the production and secretion of RANKL which will form complex with their RANK receiver for the osteoclasts differentiation and survival mechanisms [The destruction process of dental support tissues is explained by the activation of the innate cells (neutrophils and macrophages) by the use of the TLRs, mainly the TLR4, to recognize the LPS of predominant gram-negative anaerobic microorganisms in the subgingival plaque and is going to promote the transcription of inflammatory cytosine via NF-kB. Thus, this inflammatory cytosine like TNF-chanisms –5.Concomitant to the innate immunology answer, the periodontal bacteria mobilize mechanisms of the adaptive immune response, being that the professional cells (macrophages and dendritic cells) of the innate immunity, at the moment of recognizing the microorganisms by means of the TLRs, and once engulfed, have the first stimulus for their maturation, and a second cytosine profile will determine the subsequent polarization and activation of the specific antigen lymphocytes via MHC II . The subOn the other hand, the presence of Th17 lymphocytes and the secretion of its cytosine IL-17 will increase the action of the cells of the innate immunity in response to the microorganisms. The rough nature of this answer is going to produce the destruction of the bone support. Nevertheless, the IL-17 also has been found in destroyed tissues by autoimmune diseases, like rheumatoid arthritis. Nevertheless, several authors suppose that when this cytosine appears in the injury, the destruction of the dental support can be maintained, by more periodontal therapies the guest receives, causing an autoimmune periodontitis –5.Many studies relate autoimmune diseases like rheumatoid arthritis, coronary syndrome, and even erythematous lupus with the presence of periodontitis; this can prove that the phenomenon of molecular mimicry can take place in the oral cavity, being in the activation of the immune system able to destroy own tissues –12. The  S. pyogenes causes a crossed answer against cardiac myosin, promoting heart damage [The immunological tolerance for T and B cells supposes many processes of education in which these cells are eliminated if they do not recognize in the first place the histocompatibility major complexes I and II and if they recognize own structures. Consequently, the molecular mimicry does not obey this process of education. It simply happens because some microorganism structure is similar to some component of the guest, causing an autoimmune disease. An example of this process occurs when an immune response against protein m of thet damage . A. actinomycetemcomitans and to the glycoprotein-B2 being observed during the destruction of the periodontal tissue by the process of molecular mimicry, being that in this study the similarity between the leukotoxin peptide and the glycoprotein-B2 peptide is of 60% [Another example in which this phenomenon occurs is in the pancreatic beta cells, due to the crossed answer by cells T associated with viral infections in diabetes type I . A finals of 60% .The objective of this study was to verify the presence of the phenomenon of molecular mimicry between periodontal pathogens with human proteins.http://blast.ncbi.nlm.nih.gov/Blast.cgi) was employed. First, a general alignment between human proteins versus periodontal pathogens bacteria's proteins described by Socransky was performed. Later, a second individual blast was made by using DNASTAR Lasergene software  to the proteins that had similarity.The free BLAST program (http://www.ddg-pharmfac.net/mhcpred/MHCPred/) was used to evaluate the affinity of the found sequence of the Prevotella sp. hypothetical protein to be presented by the complex of greater histocompatibility of type II human where we used alleles MHC II HLA-DPA1∗01/DPB1∗04:01 and HLA-DPA1∗01:03/DPB1∗02:01.The MHCpred free program (http://swissmodel.expasy.org/) was used to accomplish the modelled of proteins. Once obtained, it is tried and it proceeded to accomplish the structural alignment for these proteins with the program PyMOL (https://www.pymol.org/)The program in real-time Swiss-Model  and later submitted to diva technique, described by Sumi et al. .A microbial peptide could be found with the BLAST method with a similarity of more than 90% to human collagen. This difference only falls to the difference of 1 amino acid for a total epitope of 10 amino acids .Prevotella sp. follows the alignment for MHC II, with percentiles major to 85%  for 7 of the obtained samples of patients with chronic periodontitis, 3 of them had lighter positive feedback (+) and 1 of them did not obtain ligation (−) .The periodontal disease, whose etiology is clearly of microbial origin, activates the immune system in such a way that it produces chronic bone resorption leading up to the complete exfoliation of the affected dental pieces, if the aggressive agent is not removed. The activation of the immune system, by consequence, begins with the recognition of the microorganisms by the macrophages with an activation pathway of the TLR2 and TLR4, leading to the apparition of inflammatory cytosines with the consequent expression of RANKL for the osteoclasts activation and survival mechanisms , 19. Nev Prevotella species has been described in the complex orange of Socransky, like bacteria that appear in the beginning stages of the periodontal disease: the gingivitis. This species owns a series of virulence factors  [The others)  able to  P. gingivalis. In consequence, two scientific articles only corroborate antibodies against Prevotella sp. in rheumatoid arthritis [The molecular mimicry, a theoretical possibility that sequences of epitopes of an organism are similar to some component of another microorganism, proves evident in a periodontal inflammation  by the arthritis , 26.Our results confirm these two findings, besides describing the epitope that generates the molecular mimicry that is not yet described in scientific literature. This epitope by consequence belongs to a protein with unknown function, whose structure seems to be very similar to the human collagen. Besides being reactive for MHC II, the similar bacterial protein similarity with human collagen has been already reported previously and studied , which i Prevotella sp. as well as observe this reaction in patients with rheumatoid arthritis.Future studies must be directed to discover the protein's function in a matter ofWith this result the existence of the molecular mimicry phenomenon in the periodontal disease can be suggested, almost in initial stages of the gingival inflammation, giving rise to autoimmune diseases that affect collagen, as a rheumatoid arthritis.


Varying speed conditions bring a huge challenge to incipient fault detection of rolling element bearings because both the change of speed and faults could lead to the amplitude fluctuation of vibration signals. Effective detection methods need to be developed to eliminate the influence of speed variation. This paper proposes an incipient fault detection method for bearings under varying speed conditions. Firstly, relative residual (RR) features are extracted, which are insensitive to the varying speed conditions and are able to reflect the degradation trend of bearings. Then, a health indicator named selected negative log-likelihood probability (SNLLP) is constructed to fuse a feature set including RR features and non-dimensional features. Finally, based on the constructed SNLLP health indicator, a novel alarm trigger mechanism is designed to detect the incipient fault. The proposed method is demonstrated using vibration signals from bearing tests and industrial wind turbines. The results verify the effectiveness of the proposed method for incipient fault detection of rolling element bearings under varying speed conditions. Rolling element bearings play a vital role in various industrial fields such as aviation, energy, and metallurgy. They generally work under varying speed conditions, which brings great challenges to the health monitoring and performance degradation evaluation of bearings . For insIn the incipient fault detection process, a health indicator is always desired to effectively compress the large amount of vibration signals and reflect the degradation trend of rotating machinery. Many studies have been conducted on constructing such health indicators. Niu et al.  exploiteAfter constructing health indicators, an alarm trigger mechanism is needed to judge whether incipient faults occur on rotating machinery or not. Various fault trigger mechanisms have been designed to deal with this problem. Ginart et al.  presenteTo deal with the aforementioned problems, this paper proposes an incipient fault detection method for rolling element bearings under varying speed conditions. The major contributions of this method are: (1) relative residual (RR) features based on extreme learning machine (ELM) are proposed, which are insensitive to the varying speed conditions and are able to better reflect the degradation trend of bearings; (2) a health indicator, i.e., selected negative log-likelihood probability (SNLLP), is constructed to fuse multiple features; and (3) an alarm trigger mechanism is established to improve the accuracy of incipient fault detection based on the analysis of the fault typical behavior.The rest of this paper is organized as follows. ELM was proposed by Huang for single hidden layer feedforward neural networks (SLFNs) . The leaL hidden nodes can be expressed as: ith hidden node, ith hidden node, ith hidden node to the output node and ith hidden node with respect to the input x. The architecture of SLFNs is shown in the The output of SLFNs with The Then, Equation (1) can be rewritten asThe number of hidden nodes L hidden nodes, the solution process of ELM parameters is listed as:For a given training sample set Step 1: Randomly generate the hidden layer parameters Step 2: Select an infinitely differentiable function as the activation function for hidden nodes and then calculate the output matrix of hidden layer Step 3: Calculate the output weight GMM has strong robustness and excellent computing performance to model the complicated internal distribution characteristic contained in the data by using several Gaussian mixture components . This chM Gaussian probability density functionsjth mixture component that satisfies the constraint n-dimensional data vector, Theoretically, arbitrary data can be expressed by To a K-means clustering algorithm is first utilized to initialize GMM parameters Step 1: The jth Gaussian mixture component is expressed asStep 2: To each training data Step 3: Update parameters kth and (k+1)th iteration, respectively.Step 4: Repeatedly iterate Steps 2 and 3 to update the parameters Once the final parameters are determined, the GMM that is trained by the sample set has been constructed. Then, the trained GMM can be used to evaluate the membership degree of new data relative to the training sample set, which is able to be denoted by the The flowchart of the proposed method is shown in In the RR features extraction module, real-time vibration signals acquired from rotating machinery are de-noised by singular value decomposition (SVD) to enhance weak incipient fault characteristics. Then, dimensional features are extracted from the de-noised signals. Since the dimensional features are easily influenced by speed variation, RR features are proposed to reduce the interference and better reflect the health degradation trend of bearings. In the health indicator construction module, a feature set is made up of RR features and non-dimensional features. Then, some features are selected from the feature set using degradation evaluation metrics, and the dimension of these selected features is further reduced by orthogonal locality preserving projections (OLPP). A novel health indicator called SNLLP is constructed based on GMM to comprehensively quantify performance degradation of bearings. In the incipient fault detection module, based on the SNLLP indicator, the incipient fault is detected by the proposed alarm trigger mechanism. More details about these three modules are presented as follows.In the RR features extraction module, at first, real-time vibration signals are acquired from sensors attached on rolling element bearings. Since bearing fault characteristics, especially in the incipient stage, are always submerged in strong background noise, SVD is employed to cancel the noise imbedded in vibration signals . Then, oThere is an approximately linear relationship between the motor speed and the extracted features whether in the normal state or fault state.The fitting-regression lines of the features under the fault state have a larger slope than those under a normal state, which means that the bearing in the fault state is more susceptible to speed than the bearing in the normal state.The experimental system is shown in In order to reduce the influence of the time-varying speed to dimensional features, the dimensional features are transformed to a set of RR features using ELM to reflect the degradation trend of rotating machinery under varying speed conditions. The process of RR feature extraction is shown in B is the number of training samples.Step 1: The speed condition parameters in the normal state Step 2: The new captured operating speed Step 3: The RR feature is constructed as Feature values in the fault state will deviate from the distribution of those in the normal state, leading to statistically abnormal changes in RR values. Therefore, faults can be detected by evaluating the extracted RR values.To evaluate the effectiveness of the proposed RR features, experimental data are further analyzed. As described above, vibration signals of bearings in normal and fault states within the 20–40 Hz speed range are first de-noised by the SVD. Then, Std of IHC and RMS are selected from the dimensional features to demonstrate the performance of corresponding RR features. The Std of IHC and RMS values in the normal state and corresponding speed parameters are used to train the ELM empirical model. The performance of the extracted RR features feature in the normal and fault state is shown in In this module, GMM-based SNLLP is constructed to provide a fusion health indicator. When the feature set composed of RR features and non-dimensional features is extracted, an optimal degradation feature selection method is required to select the features with useful information about bearing degradation processes. Good condition monitoring features should be well correlated to bearing degradation progresses, monotonous increasing or decreasing, robustness to outliers caused by noise, etc. Therefore, correlation, monotonicity and robustness metrics are utilized here for optimal feature selection. An integrated metric about these three evaluation metrics is used :(10)J=w1In order to select the degradation features that have good performance, the features whose ormation . The forAt last, the reduced principal components are fused using a GMM-based method . The GMMIn this module, based on the constructed SNLLP health indicator, a new alarm trigger mechanism is designed to detect the incipient fault based on the analysis of the fault typical behavior. For example, more abnormal fluctuation would appear in the signals and the amplitude of signals may increase gradually. The proposed alarm trigger mechanism is composed of two criteria: (1) in a sliding time window, the number of indicator values above the adaptive threshold has exceeded a safety number; and (2) the indicator values in the sliding time-window present an increasing trend, which is evaluated by the Spearman coefficient.At first, the adaptive threshold based on the Chebyshev’s inequality is established to identify outliers. For an SNLLP indicator han 1/k2 . The forAs a new sample at time Since the SNLLP indicator values always perform the gradually increasing trend during the whole life cycle of bearings, the upper bound is selected as the detection threshold. Compared with random fluctuation, more abnormal fluctuation would appear when incipient faults occur. Therefore, the first criterion is defined as follows: the number of indicator values above the adaptive threshold in the sliding time-window has exceeded a safety number. The safety number is determined by the threshold tolerance and window length, which can be calculated by the following equation:When incipient faults occur, the indicator always performs the gradually increasing trend. The Spearman coefficient between the indicator and time is an effective tool to evaluate the trend . First, Since the gradually increasing trend just performs after the incipient fault occurs, it is not a wise choice using the Spearman coefficient in all of the historical measurements. Application of the Spearman coefficient in the sliding time-window is able to increase the sensitivity of detecting the local increasing trends. Therefore, the second criterion can be defined as follows: the Spearman coefficient In this section, the proposed method is demonstrated on the incipient fault detection of generator bearings in wind turbines (WTs). As shown in Original features in The 16 time-frequency-domain features are energies (E) and energy ratios (ER) of eight frequency bands decomposed by three-level wavelet packet decomposition. The db6 is selected as wavelet basis. The sampling frequency of accelerometers in the generator bearing is 25.6 kHz. Thus, the whole spectrum is divided into eight frequency bands where each frequency band has a range of 1600 Hz.These 36 original features are classified into 22 dimensional features and 14 non-dimensional features. For the 22 dimensional features, the detailed information of the corresponding RR features extraction process is displayed as follows. The former 1000 h features and corresponding speed parameters under the normal state are used to train the ELM empirical model. Then, for a new extracted feature and speed parameter, the trained ELM empirical model is utilized to extract the RR online.In order to evaluate the performance of the proposed RR features, partial features have been intuitively compared with the corresponding RR features, which are shown in J of the 36 features are calculated and shown in J values is selected.When the feature set composed of RR features and non-dimensional features is obtained, the metric in Equation (10) is used to select the features that have a good degradation performance. Generally, the features demonstrating monotonic trends are more desired. Thus, the monotonic metric is assigned higher weights than the other two metrics, which are chosen as ectively . The evaThen, OLPP is utilized to remove redundant information in the selected 18 degradation features. The selected 18 degradation features are reduced as three principal components (PCs). The dimensionality reduction result is shown in Based on the statistic characteristics of the SNLLP values, the incipient faults are detected by the proposed alarm trigger mechanism in To demonstrate the superiority of the proposed method, another two approaches proposed by Ginart et al.  and Li eAiming at the health monitoring of bearings under varying speed conditions, this paper proposes an accurate incipient fault detection method. Based on the dependence relation analysis between speed and dimensional features, RR features based on ELM are proposed to better reflect the degradation trend of bearings under varying speed conditions. Then, the SNLLP health indicator is constructed to fuse multi-parameter features. Finally, based on the constructed SNLLP health indicator, an incipient fault alarm is triggered by the proposed alarm trigger mechanism. The proposed method is demonstrated using vibration signals from test rigs of bearings and industrial WTs. The results show that the RR features are insensitive to the varying speed conditions and are able to quantitatively reflect the degradation trend of bearings. The proposed alarm trigger mechanism is more reliable and accurate for the detection of incipient faults compared with two other alarm trigger mechanisms.Although this study has improved the accuracy of the incipient fault detection for bearings under varying speed conditions, the selection number of original features is still set subjectively. How to determine the selection number of original features adaptively is still a problem that needs to be researched in our future.