In this paper, we propose a novel gesture recognition system based on a smartphone. Due to the limitation of Channel State Information (CSI) extraction equipment, existing WiFi-based gesture recognition is limited to the microcomputer terminal equipped with Intel 5300 or Atheros 9580 network cards. Therefore, accurate gesture recognition can only be performed in an area relatively fixed to the transceiver link. The new gesture recognition system proposed by us breaks this limitation. First, we use nexmon firmware to obtain 256 CSI subcarriers from the bottom layer of the smartphone in IEEE 802.11ac mode on 80 MHz bandwidth to realize the gesture recognition system’s mobility. Second, we adopt the cross-correlation method to integrate the extracted CSI features in the time and frequency domain to reduce the influence of changes in the smartphone location. Third, we use a new improved DTW algorithm to classify and recognize gestures. We implemented vast experiments to verify the system’s recognition accuracy at different distances in different directions and environments. The results show that the system can effectively improve the recognition accuracy. Human gesture recognition and activity recognition are gradually becoming prominent promoters of human-computer interaction technology development. Traditionally, gesture recognition is usually implemented using technologies such as depth and infrared cameras ,2,3 or wExisting WiFi-based gesture recognition research can be roughly divided into two categories. One is based on Channel State Information (CSI), which is extracted from computers equipped with Intel 5300 network cards or Atheros 9580 network cards ,15. The In the former, because the Intel 5300 network card can only collect 30 subcarriers, which cannot meet the fine-grained recognition of gestures, so most of them use multiple links to increase the number of subcarriers for CSI acquisition. Multiple transmitters or receivers with multiple antennas must be deployed. In addition, the person performing gesture recognition needs to prepare a laptop in advance and walking to a specific range near the laptop for gesture recognition. On account of the hard moving of the transceiver, the difficulty of gesture recognition is significantly increased when the relative position of the person and the transceiver is changed, and the recognition accuracy will be greatly reduced.In the latter, the mobility of the smartphone can ensure the relative position of the personnel and the receiving terminal to a certain extent, which can better solve these problems. However, due to the CSI at the bottom of the smartphone cannot be obtained, the existing gesture recognition based on smartphone WiFi exploits RSSI. For example, WiGest  uses theTo break through these limitations, some researchers ,18 try tIn response to these existing problems, we hope to establish an easy-to-deploy, low-cost gesture recognition system that can be deployed on smartphones and only requires one transmitter and one receiver, leveraging the convenient mobility of smartphones to better adapt to the environment and changes in personnel positions. However, to implement such a system, we face three main technical challenges. First, the existing smartphones only can obtain RSSI and cannot extract CSI. The coarse-grained RSSI cannot guarantee the accuracy of gesture recognition. Second, the interference of the environment and personnel will affect the accuracy of the system. Finally, when different users and even the same users perform the same action, there are individual differences in gesture speed, action amplitude, and duration.To address these challenges, a novel gesture recognition system called MDGest based on smartphones is proposed. It only needs one transmitting end and one receiving end for rapid deployment. More importantly, it supports 802.11ac and allows for bandwidths of up to 80 MHz to extract 256 CSI subcarriers from smartphones exploiting nexmon firmware ,21, whicWe implemented MDGest on a Google Nexus 5 phone and evaluated its performance with sixteen different users in three typical environments. Moreover, we conduct extensive experiments, including eight Los distances, eight gesture execution distances from the smartphone, four orientations, human interference situations, and comparisons with WiGest  and WiFifirstly, we propose a novel gesture recognition system that extracts 256 CSI subcarriers from a smartphone in IEEE 802.11ac mode on 80 MHz bandwidth to implement gesture recognition using nexmon firmware.secondly, a method of extracting the main mode of CSI using cross correlation function and cross power spectral density is proposed, which can extract the time domain and frequency domain features of CSI.thirdly, we use a new improved DTW algorithm with higher computing efficiency, making our system have better adaptability to individual diversity and gesture inconsistency.fourthly, we conducted extensive experiments in three typical environments, with multiple participants under different conditions. The results show that MDGest achieves consistently high accuracies across different users in different environments and locations.In a nutshell, the main contributions of the paper are four-fold:The rest of the paper is organized as follows. Our work is highly related to wireless human sensing technology. We break these tasks into two parts: wireless sensing systems, including WiFi-based indoor positioning, tracking, and general human activity recognition; gesture recognition systems, including gesture recognition, finger gesture recognition, and handwriting recognition systems.WiFall uses features such as activity duration and rate of change in CSI values  to detecThere are other systems that can recognize more subtle activities, such as EQ-Radio , which cWith the rapid development of WiFi-based activity recognition technology, gesture recognition has gradually become a research hotspot. WiFinger uses time series Discrete Wavelet Transform coefficients combined with CSI values to recognize gestures with different numbers of finger stretches and folds . WiDraw Although these solutions provide high accuracy, they all require gesture recognition at a specified location that is relatively fixed to the transmit and receive links, and most solutions require special hardware or controlled environments. Therefore, the research of the environment and location-independent gesture recognition has gradually come into people’s field of vision. WiAG proposed a translation function to make gesture recognition independent of position and orientation . Widar 3We implemented MDGest on Android smartphones (Nexus 5) and commercial routers. We turn on monitor mode by installing the nexmon firmware on Nexus 5 phones, accessing the physical layer transmission information, and store physical layer CSI by creating new UDP frames. Then, we upload them to the host, using a Nexus 5 mobile phone to collect CSI with a bandwidth of 80 MHz in the 5 GHz of the commercial router. The total number of subcarriers collected can reach 256, and the number of available subcarriers is 234 after removing the guard bands, empty subcarriers, and pilot frequency signals. Comparing with gesture recognition using an Intel 5300 network card to collect only 30 subcarriers under a single link, MDGest dramatically improves the sensitivity and accuracy of gesture recognition.CSI is the channel property of a communication link. It describes the shadow, multipath propagation, distortion and other information that wireless signals experience during propagation. Wireless channels can be described by channel frequency response in terms of amplitude–frequency characteristics and phase–frequency characteristics. The channel frequency response can be expressed using the following formula:The gesture filter removes signals not related to gesture operations and passes signals caused by gesture operations to the next stage. We set the wake-up operation part before gesture recognition starts. After the MDGest system recognizes the wake-up operation, it starts to perform the filtering operation. The wake-up operation can quickly wake up the system, thereby ensuring a low-energy sleep state to reduce energy consumption during the non-wake phase.To correctly determine when a user is performing a gesture, we set up a wake-up detection. The device is woken up when the user puts a hand near the receiver and then making n fast up and down gestures. Quick up and down gestures can cause rapid changes in energy amplitude. We detect the start of the wake-up action by setting a threshold and then start searching for n-1 periodic energy changes. In order to better set the adaptive threshold, we used a Nexus 5 mobile phone to perform multiple sets of up and down gesture calibration experiments at a fast speed. As shown in the We first employ the Hampel algorithm to perform outlier removal on the original signal and then use a smoothing filter algorithm to smooth the signal. For each sample of the input vector, the Hampel function calculates the median of the window consisting of the sample and six surrounding samples, three on each side. The standard deviation of the median of each sample pair is estimated by using the median absolute value. If a sample is more than three standard deviations away from the median, the sample is replaced with the median. After that, a Butterworth bandpass filter is adopted to remove signals not related to gesture operations. Butterworth filter is chosen for bandpass filtering because it has the maximally flat frequency response in the passband and will not cause the largest distortion to the gesture signal. Finally, wavelet denoising is used to remove the residual noise components further while keeping the detailed patterns of the CSI. The system requires the user to have a short static interval after the wake-up action as a marker signal. We accumulate amplitude differences between adjacent time points. The cumulative value is then compared with the threshold value of the sentinel signal. We segment the gesture signal by detecting the two sentinel signals before and after. As shown in The selection of crucial subcarriers is mainly divided into the following steps: Fourier transform removes subcarriers whose frequencies are not in the frequency range of gesture recognition; periodic detection screens out subcarriers that do not correspond to the number of peaks and the number of gestures; cross-correlation detection filters out subcarriers with high correlation between different gestures. Through the screening of the above three links, we have selected the key subcarriers that are most sensitive to gesture recognition and assigned weights according to the degree of cross-correlation between these subcarriers for the gesture recognizer.We expect that our system can not only perform gesture recognition at a fixed relative position to the transmitting end but also perform gesture recognition after the position changes of users, adapting to individual differences between different users. Therefore, we need unique characteristics to describe each gesture that a user operates.x and y, which are two CSI measurements. u is their mean value. Operating the same gesture, different users, or the same users will have different operating speeds and motion amplitudes, and the change of the users position will also cause the same gesture CSI to appear differently. To remove these individual and positional differences, we use Cross Covariance Function (CCF) and Cross Power Spectral Density (CPSD) to extract the main mode features of gestures. The CCF is defined as follows:The CCF involves a multiplication between two different CSI measurements. These two CSI measurements minus the mean have a common part and a non-common part, and the multiplication of the common part always takes the same sign, so that the part is strengthened and retained. In contrast, the non-common parts of the two CSI measurements are random. Their products are sometimes positive and sometimes negative. After the average operation of mathematical expectations, they tend to “cancel” each other. This means that the CCF can extract the common parts between the two CSI measurements and suppress the non-common parts. We perform CCF calculations on the CSI measurements of the same gesture to extract common parts.The CCF describes the statistical properties of the CSI measurements in the time domain. However, it belongs to the time domain characteristic and cannot reflect the change of the gesture in the frequency domain. So we introduced CPSD, which describes the statistical properties of CSI measurements in the frequency domain.The CPSD is defined as the Fourier transform of the CCF, as follows:After calculating and intersecting CCF and CPSD of the CSI, the primary mode characteristics of the same gesture signal in the time and frequency domains are obtained and stored in a library.The MDGest system uses an improved DTW algorithm to classify gestures. DTW algorithm is a nonlinear warping method that combines time warping and distance measurement. The DTW algorithm calculates the minimum distance for two time series of different lengths. The smaller the distance between the two-time series is, the more similar the two-time series are.When using the DTW algorithm for classification, it is necessary to test the distance between gesture sequences and template gesture sequences stored in the library. The category of the template sequence with the smallest distance is the gesture category of the test sequence.The description of the test sequence and template sequence is shown in Equations (6) and (7): M data points and template sequences of N data points after feature extraction and data processing. Euclidean distance was used to calculate the distance between two sequence data points. The distance between the Equation : (9)dei,D. The value We calculate the Euclidean distance of the test sequence and the template sequence of data points to create a D is to find a path from the starting point (1) End point constraint, which requires that the starting point and ending point of the two sequences are consistent.(2) Monotonic constraint. The generation of gesture data has a sequence, and the regularization function must ensure that the matching path does not violate the generation chronological sequence of gesture data, so it must satisfy Equation .(12)f(n(3) Constraint of continuity. In order to ensure the minimum loss of matching information, the warping function cannot skip any matching point. Solving the optimization problem through the DTW algorithm, the cumulative distance of the best path can be obtained as:D, so the value of Among them, When the classic DTW algorithm is used to calculate the distance between two sequences, the DTW method requires calculation and storage of a larger matrix, and the calculation time complexity is a points ,46, therf is a constant.The Sakoe–Chiba global constraint can be understood as a restriction on the subscript in the point Under the Sakoe–Chiba constraint, the two sequences of the test sequence Since the length of the gesture data sequence may vary greatly, it is necessary to make certain constraints on the slope of the search path so that the subscript satisfies:The CM-DTW we adopted overcomes the problem of different gesture speeds and improves the calculation efficiency of DTW algorithm to calculate the distance of the gesture sequence, thereby providing a reliable measurement method for detecting the degree of matching between the CSI mode and multiple subcarriers.We evaluated the accuracy and robustness of MDGest based on the distance between the receiving and transmitting ends, the distance between users and equipment, the direction of users, and the environments. A total of 16 volunteers were recruited, including six females and ten males, aged between 20 and 28. The experiments were conducted in three indoor environments, including a laboratory, a large hall, and an office with furniture such as tables and sofas. We implemented the MDGest system on a Google Nexus 5 smartphone, which runs on Android 6.0. Furthermore, its firmware is modified with nexmon . A TP-LIWe fixed the position of the router at one end of the room, and the height was about 1 m, which is accorded with the typical indoor scene. Then, we used a Nexus 5 smartphone to collect gesture data at different distances and directions from the router. The types of gestures collected include push-pull, sweep, clap, slide, circle, and zigzag. Sixteen users collected 2160 gestures over three weeks, including different rooms, distances, and directions.There is no fixed execution time for each gesture. The timing of each gesture depended on the habits of the volunteers. This makes the system more robust to users with different gesture execution speeds. In each scenario, each user continuously collects the same gesture for 20 times, and there is a 2–3 s static interval between each time, so that the system can better divide the data.The overall recognition accuracy of six gestures in three environments is shown in Since we are using a smartphone as the receiving end, we have to consider changes in the relative position of the receiving end and the transmitting end, such as distance, angle, etc. In addition, changes in the distance, direction, and environment between users and equipment also affect system recognition performance. We also consider the false detection rate caused by environmental factors in the absence of gestures. Multiple factors have been taken into consideration from distance, over orientation to environmental disturbance, which we think could affect the recognition accuracy of the system.Since the location of the smartphone changes with the user, it is essential that the performance of the system remains stable at different distances from the smartphone to the AP. We evaluated the accuracy under the distances from 1 m to 5 m in 0.5 m steps between the transmitter and receiver. We fixed the distance between the user and the smartphone at 1 m.As shown in When users perform gestures, the same gesture will lead to different CSI changes at different distances from the smartphone. Therefore, it is vital to evaluate system performance at different distances from the smartphone. The distance between the smartphone and the AP is set to 2.5 m. The user performs gestures in the direct path between smartphone and AP under distances from 20 cm to 160 cm in 20 cm steps between hand and smartphone.Although the user is in the direct path of the smartphone and router by default when performing gestures, we still evaluate the performance of the system when the user performs gestures in other relative areas of the device. In this section, we show the effect of the user performing gestures in different orientation areas around the device on the accuracy when the device’s position and orientation are fixed. We evaluated the performance of the user in four directions of the device, which is shown in As shown in The MDGest system realizes gesture recognition depending on the changes of CSI over the LOS path. Therefore, the gesture recognition accuracy of the system is seriously affected by the interference of people in or near the LOS path. Considering the existence of human interference in the real environment, we evaluated the robustness of the system to humans disturbances. We set the distance between the receiver and the transmitter to 2.5 m. The ambient interference we set up is an interfering person walking at different distances from the transceiver link’s vertical line.As mentioned in First, when we use a PC equipped with Intel 5300 network card or Atheros 9580 network card to recognize gestures, we need to walk to a certain range near the computer to get accurate gesture recognition. This is necessary because the location of the executing gesture must be in at least one Los link so that the WiFi signal can accurately capture the signal changes caused by the gesture. It is difficult to achieve high recognition accuracy for gestures that are completely out of the Los link. However, in daily life, almost everyone carries a smartphone, so we can take out the smartphone for gesture recognition at any time, instead of preparing a laptop in advance and walking near the laptop or bringing it to us for gesture recognition. When we take out the phone, we can easily put ourselves in the Los link near the phone. From this point of view, we believe that the advantages of smartphones are far greater than laptops. Second, supporting 802.11ac and allowing for bandwidths of up to 80 MHz is a huge advantage. Using the nexmon CSI extraction tool can extract up to 256 subcarriers, which is currently only possible with 802.11ac and 80 MHz bandwidth.The current nexmon firmware is applicable to smartphones, Raspberry Pi B3+/B4, and Asus RT-AC86U with Broadcom bcm43xx series chips. For smartphones, the nexmon firmware can support many smartphone brands, including Samsung, Apple, Huawei, Google, SONY, etc. However, only some of these smartphone brands are equipped with Broadcom bcm43xx series WiFi chip. In addition, there are many other smartphone brands that cannot be supported. The previous Linux 802.11n CSI Tool  and AtheRealizing gesture recognition on smartphones can provide users with a more convenient gesture recognition experience. However, the problem of energy consumption will still bring great trouble to users. The main energy sources of the MDGest system in smartphone deployment are from two aspects. One is that the phone needs to receive the packet all the time and determine whether it is a gesture performed by the user. The other is to run feature extraction and recognition algorithms. In the former case, we have reduced some energy consumption by setting up a wake-up detection method. For the latter, we will further optimize the feature extraction and recognition algorithm in future work to reduce the computational complexity and reduce energy consumption.In this paper, we presented MDGest, a WiFi-based single-link gesture recognition system that employs CSI extracted from smartphones. MDGest leverages nexmon firmware to extract 234 CSI subcarriers from the smartphone in IEEE 802.11ac mode at 5.21 GHz (channel number 42) on 80 MHz bandwidth. Then MDGest extracts time domain and frequency domain features and exploits the improved CM-DTW algorithm to recognize the gestures. We implemented MDGest on off-the-shelf consumer smartphones and conducted comprehensive experiments. Experimental results show that MDGest can maintain high recognition accuracies across different environments, users, and locations. MDGest is robust to environmental and individual differences.In future work, we will invite volunteers of different ages to participate in the experiment, so as to expand the coverage of experimental data and improve the robustness of the system to different age groups. Furthermore, we will continue to optimize feature extraction and gesture recognition algorithms to reduce algorithm complexity and system operation energy consumption.


Antimalarial antifolate drugs have proven to be effective against malaria, however, rapid drug resistance has emerged on the two primary targeted enzymes: dihydrofolate reductase and dihydroptoreate synthase. The need to identify alternative antifolate drugs and novel metabolic targets is of imminent importance. The 6-pyruvol tetrahydropterin synthase (PTPS) enzyme belongs to the tunneling fold protein superfamily which is characterized by a distinct central tunnel/cavity. The enzyme catalyzes the second reaction step of the parasite’s de novo folate synthesis pathway and is responsible for the conversion of 7,8-dihydroneopterin to 6-pyruvoyl-tetrahydropterin. In this study, we examine the structural dynamics of Plasmodium falciparum PTPS using the anisotropic network model, to elucidate the collective motions that drive the function of the enzyme and identify potential sites for allosteric modulation of its binding properties. Based on our modal analysis, we identified key sites in the N-terminal domains and central helices which control the accessibility to the active site. Notably, the N-terminal domains were shown to regulate the open-to-closed transition of the tunnel, via a distinctive wringing motion that deformed the core of the protein. We, further, combined the dynamic analysis with motif discovery which revealed highly conserved motifs that are unique to the Plasmodium species and are located in the N-terminal domains and central helices. This provides essential structural information for the efficient design of drugs such as allosteric modulators that would have high specificity and low toxicity as they do not target the PTPS active site that is highly conserved in humans.The   Plasmodium falciparum , P. vivax, P. ovale, P. malariae and P. knowlesi. Among these five species, P. falciparum is the most pathogenic  and dihydropteroate synthase (DHPS), which are inhibited by the use of pyrimethamine and sulfadoxine antifolate drugs. The two drugs have also been used in synergy to target both enzymes simultaneously. However, resistance against the available antifolate drugs has emerged rapidly and has been observed even with the use of higher drug dosages, rendering these drugs ineffective in many cases. The combination therapy was previously used to overcome the resistance, however, in many cases, this treatment also failed as the parasite has also developed tolerance to a combination of drugs  cyclohydrolase (GCH1), which converts the GTP moiety to the 7,8-dihydroneopterin (DHNP) , DHPS, dihydrofolate synthase (DHFS) and DHFR (The first reaction of the n (DHNP) . The enzn (DHNP) . The PTPand DHFR .PTPS is a hexameric lyase enzyme that has 3-fold symmetry . The proAllostery is defined as the regulation of a protein’s structure and activity by the binding of an effector molecule at a site other than the conserved active site . AllosteProtein structures are dynamic in that they undergo large-scale domain changes in response to the binding of ligands or other compounds, thereby assuming different conformational states that allow them to perform certain functions . StudyinP. falciparum was retrieved from the online Protein Data Bank (PDB ID: 1Y13). The protein functional unit consists of six identical chains, a total of 978 residues. This structure was used to construct an ENM on the Cβ atomic coordinates of the protein, as found in the PDB file. The Cβ atoms were selected because it has been shown that they provide a better representation of the side chains orientation. A harmonic potential within a cut-off distance (Rc) of 15 Å was used to account for the pairwise interactions between all of the Cβ atoms.The PTPS crystal structure of the P. falciparum PTPS sequence as a query in a BLAST search  of H is defined as:where each super-element ijH are given by the equation:At equilibrium, the second derivatives may be calculated for the ANM using the β-carbon position vectors of PDB structures such that the elements of the off-diagonal iiH are given by the equations:And the elements of the diagonal super-elements iiH andFor the diagonal elements of iiHFor the off-diagonal elements of H can be decomposed into 3N-6 eigenvalues and 3N-6 eigenvectors that correspond to the respective frequencies and directions of the individual non-trivial modes. The modes with the lowest frequencies are termed the slowest modes and define the most collective, or global, motions of the protein. The highest frequency modes describe the more localized motions of the protein. The VMD program  calculations were performed using the DynOmics portal , specifiH is equivalent to the covariance matrix C that is composed of N × N super-elements. Each off-diagonal, ijth, super-element of H–1 contains the 3 × 3 matrix of correlations between the x-, y- and z-components of fluctuation vectors of residues i and j, while the ith super-element of H–1 describes the self-correlation between the components of fluctuation vectors of residue i. For any given mode, the mean square fluctuations (MSF) of individual residues can be obtained by summing the components of fluctuation. The MSF profiles from individual normal modes were calculated using MODE-TASK  is given by:The BIO3D R package for the exploratory analysis of the structure and sequence data  was usedi and Δrj are the displacement vectors for atoms i and j, respectively. The elements S are stored in matrix form and displayed as a three-dimensional dynamical cross-correlation map. If Sij = 1 the fluctuations of atoms i and j are completely correlated (same period and same phase), if Sij = -1 the fluctuations of atoms i and j are completely anti-correlated (same period and opposite phase), and if Sij = 0 the fluctuations of i and j are not correlated.where ΔrThe deformation energy signifies the energy density due to deformation as a function of position  given byi, rj donate the displacement of atom i and j in the mode to be analyzed, Rij(0) is the pair distance vector (Ri–Rj) in the input arrangement and K is the pair force constant.where rP. falciparum PTPS and visualized using the PyMOL Molecular Graphics System (Motif discovery was performed using Multiple Expectation Maximisation for Motif Elicitation (MEME) vs 4.11  to idents System .In this study, the ANM was applied to the structure of PTPS to classify its collective motions that have the propensity to lead the enzyme from one conformational state to another, and thus modulate its function. Further investigation of the residue fluctuations within the modes was performed to identify highly active regions that may drive these motions. The nature of allosteric modulation requires a high degree of collectivity which is often well-described by the low-frequency modes . The NMANormal modes obtained from symmetrical structures are highly susceptible to degeneracy, thus producing degenerate and non-degenerate modes. The degenerate modes share the same frequency and consequently any orthogonal transformation. In contrast, non-degenerate modes characterize unique directions of motions that often capture global meaningful motions that account for large conformational changes. Previous studies showed that dominant conformational changes of complexes were captured within the slowest non-degenerate modes . Here, wTo visualize the atomic displacements during the collective motions of PTPS, the respective eigenvectors of each of the eight non-degenerate modes were projected onto the structure of PTPS . Movies The modal analysis captured four distinctive motions that resulted in structural changes around the tunnel and the active site of PTPS. Mode 3 captured an asymmetric global twist of the terminal domains of the two PTPS trimers, characterized by their rotation in opposite directions . From viThe identified motions within the tunnel were often accompanied by structural changes in the active site region, in which the tunnel acted as a connecting vessel to allow the entry of the substrate and provide the active site with flexibility. This further demonstrates how the tunnel gating mechanism exerts such control on the active site and illustrates the functional relevance of this cavity in the catalytic mechanism of the protein.Visualization of the non-degenerate modes revealed three modes in which the wringing of the N-terminal domains promoted the surface exposure of buried regions within the tunnel. In particular, Mode 14 captured the outward and inward movement of the PTPS tunnel (likened to an engulfing movement) . Mode 17Based on our modal analysis, we suggest that the flexibility of the tunnel is essential for the efficiency of the active site. The tunnel appears to modulate entry of the substrate via expansion and contraction which is primarily driven by the rotation of the N-terminal regions, thus allowing the substrate to pass through the tunnel into the active site for catalysis. Furthermore, we hypothesize that the twist and shear motions propel the substrate through the tunnel. Taken together, these results further the understanding of the functional dynamics of the PTPS enzyme, which will serve as significant guidelines toward the design of allosteric modulators that target structural regions that are pertinent to the function of the enzyme, and therefore the life cycle of the malarial parasite.The atomic MSF profiles of the eight non-degenerate low-frequency modes were calculated and plotted . In eachDeformation is defined as any structural change caused by either an external force or a change in temperature . When thβ atom pairs is shown in A heatmap showing the cross-correlation of the CProtein motifs are evolutionarily conserved sequence patterns that might unearth a biological function. Identifying the motifs and their structural locations can, therefore, provide insight into regions that modulate protein function. It can further reveal key residues that are unique and typically required to retain the protein function and structural stability .P. falciparum PTPS and residues that are potentially involved in maintaining the secondary structure of the enzyme. The motif discovery was performed on the PTPS homologue sequences from four mammalian species including human, four bacterial species, three fungal species, and nine other Plasmodium species. A total of 27 motifs were identified. The analysis revealed four motifs (motifs 5–8) that were conserved in the Plasmodium PTPS enzyme sequences but were not detected in any of the mammalian species. Motifs 6–8 were uniquely conserved in the Plasmodium species, while motif 5 was also found among the bacterial and fungal PTPS enzymes. A heat map illustrating the occurrence of the motifs is shown in Following the analysis of the dynamics of the enzyme, we performed motif discovery to identify highly conserved motifs in The conserved Plasmodium motifs were mapped onto the crystal structure to identify their location . Motif 5The low-frequency normal modes are often associated with large amplitude conformational changes which are essential for function . The firAs the frequency increases, the modes become more localized and are accompanied by fast vibration of individual residues. It has previously been reported that high-frequency vibrating residues overlap with residues that are highly conserved and have an essential role in stabilizing the protein, and thus maintaining its function . High-frRattus norvegicus. PTPS structure (PDB ID: 1B6Z). In the P. falciparum PTPS structure these residues are equivalent to S126, T127, E129, all three of which corresponded to high-frequency vibrating residues. Overall, the combined results obtained from our NMA and motif analysis have revealed conserved residues in the Plasmodium species that have key structural significance, with strong potential to modulate the stability and function of the enzyme. Thus, the characterized regions can then be proposed as alternatives that can be targeted and kept in consideration for future drug design efforts.Previous studies established that a cluster of hydrophobic residues surrounds the active site pocket and interacts with the substrate ring . Here weHinge regions are often found between domains in a protein to allow flexibility, in which they permit the domains to move relative to one another or clamp down on a substrate . The hinTo further validate the high-frequency vibrating residues that were identified in the ANM, we used the DynOmics portal to analyze the mechanical properties of PTPS to identify the most rigid residues in the GNM . The resP. falciparum PTPS is shown in The ProDy Python package for protein structural dynamics analysis , 2014 wade novo folate synthesis pathway enzyme PTPS, in an attempt to uncover key regions that modulate conformational transitions which are imperative to its function. Notable global motions of functional significance were captured within the low-frequency non-degenerate modes. In particular, we showed that the opening and closing of the PTPS central tunnel was driven by the distinctive twisting and wringing of the terminal regions. Furthermore, the displacements observed in the PTPS N-terminal domains appeared to have a long-distance regulatory effect on the rigid core of the protein, located more than 30 Å away. Motif analysis further validated our findings with the identification of structural motifs that are uniquely conserved in the Plasmodium PTPS enzymes. These conserved motifs were located in the N-terminal domains, the central helices as well as the protein tunnel, and point toward the functional importance of these regions in the Plasmodium PTPS enzyme. Collectively these results suggest an opportunity for selective inhibition as these regions are not conserved in Human PTPS. Specifically, these regions can be proposed as potential allosteric sites for future antimalarial drug discovery attempts.Antifolate drug resistance is a major challenge in the fight against malaria and the need to develop new drugs with a unique mechanism of action has become more crucial than ever. In this study, we classify the dynamics of the parasite’s All datasets presented in this study are included in the article/ÖTB conceived the project. AK did the calculations and analysis of data under the supervision of CR and ÖTB. AK wrote the first draft. All authors edited it to the final version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


F1 score of up to 93.05%. The results show that the proposed attention mechanism effectively improves the fine-grained classification of crop disease images.Fine-grained image classification is a challenging task because of the difficulty in identifying discriminant features, it is not easy to find the subtle features that fully represent the object. In the fine-grained classification of crop disease, visual disturbances such as light, fog, overlap, and jitter are frequently encountered. To explore the influence of the features of crop leaf images on the classification results, a classification model should focus on the more discriminative regions of the image while improving the classification accuracy of the model in complex scenes. This paper proposes a novel attention mechanism that effectively utilizes the informative regions of an image, and describes the use of transfer learning to quickly construct several fine-grained image classification models of crop disease based on this attention mechanism. This study uses 58,200 crop leaf images as a dataset, including 14 different crops and 37 different categories of healthy/diseased crops. Among them, different diseases of the same crop have strong similarities. The NASNetLarge fine-grained classification model based on the proposed attention mechanism achieves the best classification effect, with an  Outbreaks of crop disease have a significant impact on the yield of agricultural production. Often, large-scale disease outbreaks destroy crops that have taken considerable efforts to grow, causing irreparable damage. Even without large-scale disease outbreaks, small-scale emergence can cause serious losses to crop yield and quality  the similarity between the sub-categories under the same disease category is very strong; (2) the field environment has significant background interference; and (3) the location of different crop diseases is inconsistent.In an attempt to overcome these difficulties, many researchers have applied convolutional neural network (CNN) to crop disease classification. To investigate the impact of dataset size and species on the effectiveness of crop disease classification based on deep learning and transfer learning, Barbedo  showed tIn recent years, it has been found that human cognitive processes do not focus attention on the entire scene at one time. On the contrary, they pay more attention to local regions in the scene while extracting relevant information. Models based on attention mechanisms have achieved good results on many challenging tasks, such as visual question answering   and the testing using images of actual field conditions , and then performed training using only images of actual field conditions  and testing with images taken under laboratory conditions .VGG, ResNet, and other deep CNN models have achieved great success in image classification. The pre-trained deep CNN model has been fully trained on a large image dataset (ImageNet), allowing many features required for image classification to be learned. Therefore, we can use the idea of transfer learning to fully utilize the large amount of knowledge learned by pre-training the CNN model on the ImageNet dataset, and apply it to crop disease image classification. This paper describes how the transfer learning method of parameter transfer was adopted to remove the maximum pooling and fully connected layers after the final convolution, and introduces a new fine-grained classification model based on the attention mechanism. Compared with the random initialization of the weight parameters of each layer of the network, the fine-tuning method helps accelerate the convergence of the network.For image classification, there are several CNN baseline models that have been successfully applied to specific tasks. Regarding the task of image recognition and classification of crop diseases, six CNN models that were pre-trained using ImageNet have been applied: (1) VGG16 and VGG19  to obtain the required detailed information about the target, with other useless information suppressed. Soft attention is most commonly used, because this is a completely differentiable process that can realize end-to-end learning in CNN models. Most soft attention models learn an attention template to align the weights of different regions in a sequence or an image and use this template to locate the distinguishable regions. Different from soft attention, the hard attention mechanism is a random, non-differentiable process that determines the importance of individual regions one at a time, rather than identifying the important regions within the whole image.For image classification, the weight of the arithmetic mean of attention can be extracted through attention learning to form the attention spectrum of the image. Similar to traditional natural language processing, the image-based attention can be obtained through the model illustrated in I is the input image. The attention model has n parameters, a1, a2, ⋯ , ai, an, which respectively represent a description of each part of the image. O is the return value of the model's attention spectrum , which is determined from the importance of each ai relative to the input I. By filtering the input image through this output, the region that requires most attention can be identified.In Based on the pre-trained model described in Section transfer learning and the attention mechanism introduced in Section attention mechanism, this paper proposes a fine-grained classification model based on the attention mechanism . By learThe attention model proposed in this paper adopts an unsupervised training mode. There is no pre-labeled ground truth to constrain the attention spectrum, so there is no separate loss calculation. Instead, a backpropagation adaptive mode is used to constrain the weight distribution of attention. The loss function defined in this paper is expressed as:n = 37 is the number of input samples, x is the input sample, ytruth is the actual category, and ypred is the predicted category output by the final layer of the network. In the process of backpropagation, the output error of the Softmax layer is backpropagated, and the parameters are updated using the random gradient descent method, so that the final loss function value decreases and the network converges.where f ∈ ℝH×W×C, where H and W refer to the height and width of the feature spectrum of this layer, C refers to the number of channels of the feature spectrum of this layer, and for each position  on the spectrum, its feature value is expressed as Wm,n can then be obtained as:Through the soft attention mechanism, the output of the final convolutional layer of the CNN is obtained. This is taken as the input of the attention model, and the corresponding attention spectrum is calculated. The original feature spectrum is then weighted by the attention spectrum, and the output attention feature spectrum is provided as the input for the subsequent network. Let the feature spectrum of the output of the final convolution spectrum after the pooling operation be expressed as ATT is a mapping function learned by the attention model and Watt is the weight parameter of the attention model. Through Softmax regression of wm,n, the final attention spectrum M =  is obtained as a normalized probability matrix, where Mm,n is expressed as:where Softmax layer. The kernel sizes of the convolutional layers are 3×3 and 1×1. The attention feature spectrum M by the CNN feature spectrum f, and is expressed asAs can be seen from The 3×3 convolution kernel further extracts the CNN feature of the final convolution spectrum. In order not to reduce the feature receptive field and feature information, a convolution kernel with the same size as the original network is selected. Compared with the 3×3 convolution kernel, the 1×1 convolution kernel enables information interaction and integration across channels. By connecting features in the channel direction, nonlinear components can be added to features to improve the feature expression ability of the attention model.The attention spectrum of the final convolution spectrum of the CNN is obtained through the attention model, and the attention spectrum and the original CNN feature spectrum are then multiplied to obtain the attention feature spectrum. The attention spectrum is the spectrum obtained after normalizing the weights of the features. According to this definition, the attention feature spectrum obtained after multiplication has a certain attenuation compared with the original CNN feature spectrum. Additionally, during the convolution and probability calculation, the spatial transformation of the CNN feature spectrum and noise addition means that the calculated attention spectrum may be distorted. In this case, the obtained attention spectrum has no guiding significance for the original image spectrum. To overcome this problem, once the attention feature spectrum has been obtained, the original CNN feature spectrum is added and fused to obtain the final attention By adding the attention spectrum to the CNN feature spectrum, the distortion of the attention spectrum is overcome and the original feature spectrum before the fully connected layer can be effectively utilized.By extracting and merging the attention spectrum, a spectrum of features is obtained that is well-located and noticeable in space. This spectrum is then input to the subsequent fully connected layer. As the connection operation maps the convolutional spectra of all channels to one point in the fully connected layer, the spatial information is destroyed by the operation of the fully connected layer. The original intention of introducing the attention model is to extract and improve the significant regions of the CNN feature spectrum in space. However, after the fully connected layer, the spatial information of the extracted attention feature has also been destroyed. Therefore, the attention space feature is reused by connecting the attention feature spectrum in the final fully connected layer, as shown in F1 evaluation index were used to evaluate the crop disease image classification model. The F1 value is the harmonic average of the precision and recall, and has a maximum of 1 and a minimum of 0. It is calculated as follows:The accuracy, precision (P), recall (R), and comprehensive In all our experiments, we preprocessed the images to sizes of 256×256, 299×299, and 331×331 pixels, conducted a total of 1,000 training epochs, and used a batch size of 32. We used a momentum SGD initial learning rate of 0.001. When the standard evaluation stopped increasing, the learning rate was multiplied by 0.1 until it had dropped to 0.0001. After lowering the learning rate, we waited for five epochs before returning to normal operation. If the loss of the test set did not improve after 20 epochs, the learning rate was reduced. We conducted experiments using multiple pre-trained models, all of which are robust to the selection of hyper-parameters.F1 values of various models on the test set. The results indicate that the fine-grained fine-tuning classification models based on the attention mechanism outperform the original pre-trained models by 1–2% in terms of accuracy, precision, recall, and F1 value. This demonstrates that the attention mechanism improves the classification performance of the models and allows them to focus on key regions in the image. The fine-grained NASNetLarge model based on the fine-grained attention mechanism achieves the highest accuracy, precision, recall, and F1 values, and thus provides the best classification performance. These 12 models were further trained using only the original image to record the training period for the best performance. As shown in We also comprehensively evaluated our algorithm on public plant datasets of Flavia . In contrast, when the laboratory condition images are used for training and the field condition images are classified, the classification performance is obviously reduced . This shows that image classification under field conditions is a more difficult and complicated task than the classification of images obtained under laboratory conditions, and proves that the construction of an efficient automatic detection and diagnosis system for crop diseases using images obtained under field conditions is of great significance.When only laboratory or field condition images are used for training, the accuracy on the test set is significantly lower than when both laboratory and field condition images are used for training. The results show that the model can obtain better performance when using images obtained under field conditions for training and requiring classification of laboratory condition images  shadows on the leaves in the images, with some images appearing dark and shaky; (2) other objects in the image that are not related to the leaf itself, such as a trunk, fruit, or fence. Note that these problematic images occupy a very small portion of the dataset. In short, according to the certainty levels provided by the final model, the attention mechanism-based approach proposed in this paper overcomes these problems in most cases.A typical case is category 8 . F1 value 93.05%) in the classification of the 11,640 images in the test set. The fine-grained NASNetLarge neural network model based on the attention mechanism achieves excellent classification performance by analyzing simple leaf images, so it is highly suitable for the automatic detection and diagnosis of crop diseases. In addition, the experimental results show that the images taken under field conditions in the training set are of high importance, indicating that when training such models, the proportion of images obtained under field conditions in the training set should be carefully considered.This study constructed, trained, and tested a fine-grained neural network model based on the attention mechanism for the classification of simple leaves of healthy or diseased crops. The model was trained using 58,200 publicly available images obtained under both laboratory conditions and field conditions. The data include 14 crop species in 37 different categories of  combinations, including some healthy crops. The optimal model was found to be a fine-grained NASNetLarge neural network based on the attention mechanism, which achieved an accuracy level of 95.62% (precision 94.35%, recall 91.79%, For the backbone network of NASNetLarge, the results show that the NASNetMobile neural network model, similar to NASNetLarge, achieves state-of-the-art classification results on related datasets, surpassing the performance of previous lightweight networks such as MobileNet (Sandler et al., Although the system developed in this study achieved a high success rate, it is far from becoming a universal tool under actual field conditions (Boulent et al., The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.GY and YH: conceptualization. GY: methodology, software, formal analysis, data curation, writing—original draft preparation, and visualization. GY and BX: validation. GY and YY: investigation. YH and YY: resources, supervision, and project administration. BX: writing—review and editing. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Feature extraction is one of the challenging problems in fault diagnosis, and it has a direct bearing on the accuracy of fault diagnosis. Therefore, in this paper, a new method based on ensemble empirical mode decomposition (EEMD), wavelet semi-soft threshold (WSST) signal reconstruction, and multi-scale entropy (MSE) is proposed. First, the EEMD method is applied to decompose the vibration signal into intrinsic mode functions (IMFs), and then, the high-frequency IMFs, which contain more noise information, are screened by the Pearson correlation coefficient. Then, the WSST method is applied for denoising the high-frequency part of the signal to reconstruct the signal. Secondly, the MSE method is applied for calculating the MSE values of the reconstructed signal, to construct an eigenvector with the complexity measure. Finally, the eigenvector is input to a support vector machine (SVM) to find the fault diagnosis results. The experimental results prove that the proposed method, with a better classification performance, can better solve the problem of the effective signal and noise mixed in high-frequency signals. Based on the proposed method, the fault types can be accurately identified with an average classification accuracy of 100%. Rolling bearing plays an important role in the rotating machinery as it has a pointedly effect on its service life. Among the causes of abnormalities in rotating machinery, 30% of abnormalities are caused by bearing failure. Hence, the way for quick and effective fault diagnosis has been one of the emphases of research among scholars worldwide. When a rolling bearing failure occurs, its vibration signal changes, and its fault information is contained in these changed vibration signals. Under increasingly complex working conditions for rotating machinery, it is difficult to diagnose faults in rolling bearing with only individual subjective experiences. Hence, signal analysis is a necessarily process for accurate fault diagnosis. Meanwhile, the vibration signal of bearings often contains abundant noise signals due to the complex working conditions, which adds a great deal of difficulties to the failure form and performance prediction of rolling bearings, affecting the accuracy of judgment. Hence, noise reduction is quite vital and meaningful before the diagnosis .In addition to the frequently-used time domain  and freqThe EEMD method results in signal decomposition with anti-noise characteristics, reduces reconstruction errors, and improves the quality of IMFs by adding Gaussian white noise to the original signal. Therefore, the EEMD method is widely used in signal processing and fault diagnosis . HoweverAlthough the wavelet threshold denoising method effectively removes noise from high-frequency signals, the effect is not ideal, and the useful signals still mix with noise signals. Nevertheless, the wavelet semi-soft threshold with the advantages of the hard and soft threshold can not only preserve the integrity of the signal but also ensure the accuracy of noise reduction . HoweverAiming at the problem that the bearing features masked in the nonlinear signals cannot be effectively extracted, to ensure accurate fault diagnosis, we need to research from the perspective of signal complexity. So far, many scholars have proposed many methods to characterize the complexity of signals, and entropy theory is one of them . EntropyCosta et al.  proposedTiwari et al.  proposedWith the increasing complexity of today’s mechanical equipment and working environments, where rolling bearings work under hostile conditions, the vibration signals are often more complex and irregular. Therefore, it is difficult to directly use the entropy methods to extract fault features that can effectively distinguish fault types. For this problem, some scholars preprocessed the original vibration signals before using entropy theory for feature extraction. Hsieh et al.  utilizedAouabdi et al.  used MSEIn summary, to obtain higher accuracy in rolling bearing fault diagnosis under complex working conditions, a method based on signal reconstruction and the MSE is presented in this paper. First, the EEMD is applied to decompose the vibration signal. Secondly, the WSST is used to denoise the modal components in the high-frequency signal filtered by correlation analysis, to reconstruct the signal. By using the EEMD-WSST method, the shortcoming of EEMD can be avoided and all the advantages of these two methods can be preserved. Different from the conventional signal processing method, the noise in the reconstructed signal is expertly filtered on the basis of ensuring the integrity of the effective part in the high-frequency signal, and this improves the efficiency of the noise reduction method. Finally, based on the effectively denoising, the MSE method is used for calculating the MSE values of the reconstructed signal. To distinguish the fault types of bearings, the calculated sample entropy is input as a rolling bearing fault feature into an SVM model, which is more suitable for training small sample data . CompareThe remainder of this paper is organized as follows: The EMD decomposition is to process the signal smoothly to obtain multiple data sequences under different characteristic scales. These data sequences are called IMFs. Any signal can be regarded as a combination of several IMFs, and the IMFs obey the following two conditions: (1) the number of local extrema and the number of the zero-crossing points are equal or of only one difference in the whole time; (2) at any time, the two envelopes determined by the local maximum point and local minimum point have an average value of 0. The EEMD method is essentially an improved EMD method. It adds white noise into the signal mainly according to the characteristic that the mean value of white noise is zero and still decomposes the signals with EMD and averages the decomposition result. The more average processing times, the less impact of noise on the decomposition result. The EEMD is to perform multiple EMD decompositions on the signal added Gaussian white noise. The EEMD is to resort the statistical properties of the even distribution of the frequency of the Gaussian white noise to ensure the noise-added signal has continuity on different frequency scales, thereby reducing the mode mixing degree of the IMFs. x(t). The specific decomposition steps are as follows:The EEMD method equalizes noise based on the distribution characteristics of white noise spectral equalization, to make the frequency distribution tend to be uniform. Mode mixing is caused by the distribution points of the amplitude of different signals with white noise added. By using the characteristics of the EEMD method, the high-frequency modulation information is adaptively separated. This method not only weakens the mode mixing effect of the EMD method but also reduces the error caused by the central frequency band and filter band selection error in resonance demodulation. Let the signal be Step 1. Set the average processing times of Step 2. Add random white noise j-th IMF in the i-th decomposition, and i-th decomposition.Step 3. Decompose the new signal sequence M-th decomposition to offset the noise and get the final IMFs.j-th IMF obtained by the EEMD method, Step 4. Sum and average the corresponding IMFs obtained by the Step 5. The final result of the The EMD and EEMD methods are essentially signal decomposition methods and do not have the function of removing or reducing noise compositions. These two methods decompose the original signal into multiple basic modal components, directly remove the basic modal components with higher noise components, and reconstruct the remaining basic modal components to realize the denoising process. However, these two methods will cause the loss of value signal information in high-frequency signals. The EEMD can better remove the background noise; however, it will cause the loss of the effective information in the high-frequency signal. Compared with EEMD, although wavelet denoising can better retain the effective information in the signal, its threshold influences the original signal denoising. Therefore, based on the wavelet denoising, the threshold function of wavelet denoising is improved, and a denoising method combining EEMD and improved wavelet denoising is applied to reduce the noise in the bearing signals.Wavelet transform is suitable for nonlinear signal processing. Wavelet threshold denoising can perform noise reduction processing on the signal according to the different amplitude–frequency characteristics of the signal and has a strong ability to inhibit white noise. Therefore, wavelet threshold denoising is introduced into the noise reduction processing of the high-frequency signal to reconstruct the original signal. The most widely used wavelet transform in the signal denoising field is the wavelet threshold denoising. This is used, essentially, to filter the signal. The wavelet threshold method is to transform the signal to the wavelet domain; then obtain the wavelet coefficients and filter out noise; and finally, reconstruct the signal. The wavelet threshold function mainly includes the soft threshold as shown in Equation (5) and the hard threshold as shown in Equation (6).As shown in Equation (6), the treatment method of the hard threshold is to keep the wavelet coefficients above the threshold unchanged and change the wavelet coefficients below the threshold to 0. However, this “guillotine” method will cause changes in the wavelet domain and lead to sudden local changes in the noise reduction results. The treatment method of the commonly used soft threshold is to change all the wavelet coefficients less than 3σ to 0 and, in a unified way, subtract 3σ from the wavelet coefficients greater than 3σ to make the wavelet coefficients smooth in the wavelet domain. Although this will cause some loss of useful information in the high-frequency signal, it eliminates the local concussion caused by the hard threshold function. T1 < T2. The two traditional threshold acquisition methods each have advantages but also have damage to the signal. The hard threshold method provides better protection for the edge information of the original signal, while the soft threshold method smooths the signal edge after noise reduction. The disadvantage is that the soft threshold method causes a certain degree of distortion. It is obvious that the selection of wavelet threshold plays a decisive role in the whole wavelet threshold denoising process and determines the final effect of the signal denoising method. Therefore, a method is proposed to improve the above defects; this expression is as shown in Equation (7), where 0 <The wavelet semi-soft threshold function combines the advantages of the two methods while cleverly avoiding their defects. The images of the three threshold functions are shown in In the vibration signal, noise mostly exists in the high-frequency band. How to screen the IMF with high noise content from multiple IMFs is very important for the subsequent denoising process. Although EEMD can decompose signals by modal and effectively suppress mode mixing compared to EMD, there is no explicit method to determine the demarcation point of EEMD signal-to-noise components, and it is necessary to further distinguish between signal components and noise components. The function characteristics of the autocorrelation function of each modal component roughly determine the signal-to-noise demarcation point of the input signal. Then, the improved method is used to denoise the noise components after separation. Finally, the signal after denoising is recombined with the remaining components to obtain the final denoising result. This method retains the advantages of EEMD in effectively eliminating the background noise and overcoming the shortcomings of wavelet threshold denoising, which cannot completely eliminate the background noise. It also retains the advantages that wavelet threshold denoising can well retain the useful signals in the original signal and overcomes the shortcomings of EEMD forced denoising and loss of useful signals. The combination of the two achieves a better denoising effect by determining the noise component through the autocorrelation function. In this section, the Pearson correlation coefficient is used to analyze the correlation between the IMF component and the original signal to screen the IMF component.The Pearson correlation coefficient is one of the indexes that can describe the degree of correlation between two random variables; therefore, it can be used as a standard that can accurately measure the correlation between two signals. Assume that for two random signals, X and Y, the Pearson correlation coefficient can be expressed asAs shown in Equation (8), if the value of the Pearson correlation coefficient The linear relationship between the two signals in the high-noise components can be divided into the following three kinds: if To quantitatively evaluate the performance of the denoising method, the indexes for measuring the denoising effect generally include the SNR and the root mean squared error (RMSE). The SNR reflects the energy relationship between the signal and the noise, and the RMSE reflects the magnitude of the average energy of the noise. Generally, the method with a high SNR and a low RMSE is better than the method with a low SNR and high RMSE. The mathematical expressions of the SNR and the RMSE are expressed as Equations (9) and (10), respectively.The EEMD-WSST signal reconstruction method can be briefly summarized as follows: Step 1. Decompose the original signal into the IMFs and residual components by EEMD.Step 2. Obtain corresponding high-frequency noisy IMFs by Pearson correlation coefficient analysis and perform wavelet semi-soft threshold denoising on the high-frequency noisy IMFs.Step 3. Perform signal reconstruction on the noiseless IMFs, other IMFs, and residual components.This method reflects the advantages that the wavelet denoising method has for multi-resolution applications. EEMD adaptively decomposes the signals based on the original signal and, meanwhile, avoids the defects in practical applications, inhibits the mode mixing, and improves the denoising effect while ensuring signal integrity.To verify the feasibility and effectiveness of the EEMD-WSST signal reconstruction method, a simulating rolling bearing signal is constructed. It can be described by Equation (11).When To verify that the proposed method has a better noise reduction effect compared with other traditional methods, the following methods are compared experimentally: EEMD forced denoising, wavelet threshold denoising, EEMD combined with wavelet hard threshold (EEMD-WHT), EEMD combined with wavelet soft threshold (EEMD-WST), EMD combined with wavelet semi-soft threshold (EMD-WSST), and EEMD-WSST.The time domain and frequency domain chart of the simulated signal, after EEMD forced denoising, are presented in The time domain and frequency domain chart of the simulated signal after wavelet threshold denoising are presented in The EEMD-WSST signal reconstruction method is based on correlation analysis. Pearson correlation analysis is performed on the original signal and the IMFs obtained by EEMD, and the wavelet threshold denoising is performed on IMF with the Pearson correlation coefficient greater than 0.7. Then, we reconstruct the signal. The correlation analysis results are shown in In the EEMD-WSST, the IMFs with a Pearson correlation coefficient greater than 0.7 are selected for wavelet semi-soft threshold denoising. Therefore, IMF1 and IMF2 are selected.The time domain and frequency domain chart of the simulated signal after denoising by EEMD-WHT are shown in In As shown in The results show that after denoising by the EEMD-WSST, the SNR of the signal is 4.3782, and the RMSE is 1.1196. Compared with wavelet threshold denoising, EMD forced denoising, EEMD forced denoising, EEMD-WHT, and EEMD-WST, the SNR of the signal is higher, and the RMSE is smaller. This shows that the EEMD-WSST signal reconstruction method can achieve a better denoising effect.The feature extraction based on multi-scale entropy is to calculate the sample entropy values of the coarse-grained vectors on multiple time scales, which is obtained from the reconstructed signal. The specific calculation steps are as follows:Step 1. Build coarse-grained vectors. According to the EEMD-WSST signal reconstruction method, the reconstructed signal is The process of coarsening is equivalent to using a sliding window of length k is the integer between Step 2. Calculate the distance between Step 3. Calculate Step 4. Calculate the MSE. MSE is defined as the sample entropy at different time scales For signal data with length N, its MSE value is presented in Equation (17).The main parameters affecting MSE are embedding dimension (1) The embedding dimension (2) The similarity tolerance (3) The scale factor Based on the advantages of signal reconstruction, the MSE feature extraction technique can retain the signal integrity to the maximum extent on the basis of filtering the high-frequency signal noise and can effectively reflect the complexity of the internal information of the rolling bearing. SVM has the advantages of being able to deal with small samples, non-linear characteristics, and fast classification speed. Therefore, SVM is used to test the classification performance and verify the effect of signal reconstruction and feature extraction methods in this paper. The fault diagnosis model of rolling bearings is as follows:Step 1. Decompose the signal Step 2. Perform the Pearson correlation analysis on each IMF to obtain the Pearson correlation coefficient of each IMF Step 3. Reconstruct a new eigenvector; Step 4. Perform coarse-graining on the eigenvector Step 5. Determine the embedding dimension Step 6. Input the obtained MSE into the SVM model to distinguish the fault types of the rolling bearings.The flow of the presented model is presented in The experimental data for rolling bearings analyzed in this study were provided by the Case Western Reserve University Bearing Data Center . The tesThe rotation frequency corresponding to each type of fault and its fault frequency can be clearly observed from the frequency waveform of Time-domain and frequency waveforms of the reconstructed signals of different fault types after denoising are shown in The MSE of the four bearing fault types was respectively calculated, and the scale factor was set as 6. It is unreliable to identify different bearing fault types from the time-domain waveform. The different fault types are easy to distinguish, and the four MSE value curves overlap only at the beginning. A total test dataset composed of 200 groups of four rolling bearing fault types was collected by the accelerometer, and the collected vibration signal data were intercepted in segments. The sample length of each group was 1024. The statistical results obtained from 10 experiments are presented in Compared with SE-OFS, the classification accuracy has been greatly improved. The average classification accuracy of different methods is given in SE-EEMD has a total of five misclassified samples, and the average classification accuracy is 98.75%, which is greatly improved compared with the former two, indicating that the EEMD plays an important role in improving the classification accuracy. MSE-OF has a total of seven misclassified samples, which are distributed in inner race fault and outer race fault. The average classification accuracy is 98.25%, and the number of misclassified samples is nearly 11 times less than the SE-OF. There are only three misclassified samples in MSE-EMD, and only inner race fault is misclassified. The average classification accuracy is 99.25%, which is slightly higher than when using only the SE method. MSE combined with EEMD-WSST is the proposed method, as the number of misclassified samples is zero, and the average classification accuracy is 100%. According to the statistical results, it can be found that the average classification accuracy of the SE method is significantly lower than the MSE method, and single-scale SE method is not enough for distinguishing different fault types. Relative to the single-scale method, MSE method has better classification performance. In particular, the proposed method can achieve 100% accuracy. With the proposed method, the noisy signals are eliminated by signal reconstruction, and then the SE values are calculated on multiple scales to obtain better classification results.Taking into account the results of other techniques tested on the same vibrations signals, the method proposed in this paper is either better than or comparable to other available techniques that usually deploy much more complex algorithms, as can be seen from To verify the effect of the proposed method in bearing fault diagnosis under the actual working conditions, the bearing fault simulation experiment is carried out using the rotating machinery test bed. The test bed mainly includes a SGM7J-04AFC6S servo motor with a rated output of 400 W, rated current of 2.5 A, rated torque of 1.27 N, and rated speed of 3000 RPM; YMC122A100 accelerometer with a frequency range of 0.310 kHz; POD-0.6 Kg magnetic powder brake with a rated voltage of 24 V, rated current of 0.81 A, rated torque of 6 N·m, speed at 2000 r/min; plum coupling base and bearing chock, connectors, and fasteners; COCO80 data collector, etc. The test bearing is the 7204C/P5 bearing, and the sampling frequency is 1024 Hz. The specific parameters are shown in The time domain and frequency domain chart of four rolling bearing fault types are presented in The time domain and frequency domain chart of the reconstructed signal under different fault cases after denoising by the EEMD-WSST are presented in From A total of 200 groups of test data of rolling bearing fault state are collected by accelerometer, the collected vibration signal data are intercepted in segments, and the sample length of each group is 1024 points. The fault features extracted by six different feature extraction methods are respectively input to the SVM classifier for comparative analysis, and the results are given in The average classification accuracy of different methods is given in SE-EEMD has a total of 26 misclassified samples, and the average classification accuracy is 93.5%. Compared with the former two, the number of misclassified samples is significantly reduced. MSE-OFS has a total of three misclassified samples, and the misclassified samples are only in the inner race fault case. The average classification accuracy achieves 98.25%, which is 10% higher than SE-OFS. MSE-EMD has a total of two misclassified samples, which are in the normal case and inner race fault case. The average classification accuracy is 99.05%. Compared with the method using SE, the overall classification accuracy is significantly improved. MSE combined with the EEMD-WSST signal reconstruction is the proposed method, the number of misclassified samples is zero, and the average classification accuracy is 100%.The experimental results indicate that the proposed method is reliable and effective. In comparison to the traditional methods, the EEMD-WSST signal reconstruction has more advantages in distinguishing the different rolling bearing fault types under different conditions. MSE can effectively measure the complexity of signals and had a better performance in our experiments. In comparison to the SE, MSE has better performance on extracting the nonlinear fault features of the vibrational signals. The experimental results show that the classification accuracy is improved upon using MSE values of the vibrational signals after signal reconstruction, compared to directly using the MSE values of the original vibrational signals. Especially for combining with the EEMD-WSST signal reconstruction, the classification accuracy is higher than combining with other signal reconstruction methods. The results of two cases support this analysis. For better feature extraction of the bearing and more accurate identification of the bearing fault types, a method based on the EEMD-WSST signal reconstruction combined with the MSE feature extraction is proposed in this paper. The nonlinear and non-stationary fault signals of bearings are decomposed by the EEMD method, and the IMFs in each state are obtained. The IMFs containing rich fault information are screened by correlation analysis, and the reconstructed signals are obtained after denoising by the WSST denoising. The simulation results indicate that the noise can be effectively filtered by the EEMD-WSST. The eigenvectors are obtained by calculating the complexity of the reconstructed signal on multiple time scales by MSE, and the eigenvectors are input into the SVM classifier model to identify fault types. Through the verification of the two groups of experiments, we compared the SE and the MSE and, respectively, combined the original fault signal, EEMD, and the EEMD-WSST. The experimental results, which used CWRU datasets and the measurement dataset from laboratory, show that the signal reconstructed by the EEMD-WSST signal reconstruction can better express the fault information of the rolling bearing. When the basis function and layer numbers of WSST were wrongly chosen, the diagnosis accuracy would to become lower. Meanwhile, compared with the SE, the MSE can better extract and reflect nonlinear fault features. Relative to the MSE of the original vibration signal, the MSE of the reconstructed signal can better suppress the random noise and ensure the integrity of the original signal and accurately measure the complexity of the rolling bearing signal under different time scales. Therefore, the fault diagnosis method based on EEMD-WSST signal reconstruction and the MSE has a better classification performance and has prospects for application in the field of rotating machinery. We intend to apply the method to other datasets collected in the project to further verify the effectiveness and generalization ability of this method. In our future work, we will focus on researching more effective and high-performance fault diagnosis methods for rolling bearings.


Escherichia coli and Staphylococcus aureus, respectively. Additionally, the mean loss obtained depended on whether diagnostic costs and reduced feed intake in cases of mastitis were included in the clinical mastitis loss calculation. The monetary values of labor cost, drug cost and culling cost, as well as treatment price , significantly influenced the clinical mastitis losses per case. All other tested moderators were not associated with mastitis losses, highlighting the need for more standardized economic studies, for both methods and ways results are presented, and suggesting that the mastitis losses assessed in the literature cannot be extrapolated . Although meta-analyses are useful to overview the burden of diseases across studies, their ability to summarize extensive literature with various economic assessments is limited. These limitations in loss assessments also suggest the need to focus on management strategies rather than on pure monetary estimations of disease costs, at least for production diseases at the farm level.The literature contains an extensive panel of studies focusing on the costs of animal diseases. The losses of an agriculture holding can be influenced by many factors since farming is a complex system and diseases are closely interrelated. Meta-analysis can be used to detect effects  across studies and to identify factors that may influence those effects. This includes the external validity of the published study results with regard to the input parameters and the internal validity of the study, particularly how other diseases related to the target disease were accounted for. Mixed-effect meta-regressions were performed to estimate the mean clinical mastitis losses per case across the literature and to elucidate to what extent clinical mastitis losses are influenced by (i) general factors, such as etiology; (ii) the types of losses that contribute to the total mastitis losses; and (iii) prices. In total, 82 observations from nine studies were included in the meta-analysis to assess mean clinical mastitis losses per case. The multivariate meta-regression showed that etiology significantly influenced the clinical mastitis loss per case. The mean loss was determined to be €224 per case for all published etiologies. In detail, mean losses equalled €457 and €101 per case of clinical mastitis due to gram-negative and gram-positive bacteria, respectively, and €428 and €74 per case of clinical mastitis due to  Some reviews show large variations in the calculated impact of animal diseases, such as subclinical ketosis bovine viral diarrhea –3. ThereMastitis is one of the most important diseases in dairy farms worldwide , 10; it The present work focuses on the factors that may influence losses due to animal diseases, using clinical mastitis as an example, to define whether the present state of the economics of this disease can be adapted to elucidate the (i) internal validity of the study (what is accounted for during the economic assessment) and (ii) the external validity of published study results with regard to the input parameters used. It aims to describe the usefulness of meta-analysis to evaluate which factors may influence the estimated losses due to mastitis infections in the dairy population according to the literature.Publications on the losses due to clinical mastitis were selected from English-language literature up to June 2019. The literature search was conducted in PubMed, Science Direct, and Google Scholar. The keywords were applied separately or in different combinations for the literature search in the three databases. Subsequently, the reference lists of the identified studies were also screened. All the studies were analyzed according to the following inclusion criteria: (i) the publication included clinical mastitis and presented results for clinical mastitis separately from subclinical mastitis if both were included; (ii) the publication included the monetary losses presented per clinical mastitis case or per year and cow; (iii) the publication results were obtained in temperate-climate countries; and (iv) the publication data was obtained after 1990 to represent the modern livestock system. No restrictions were set on the intensification level (milk production), the level of monetary losses due to clinical mastitis, or the currency used. Publications focusing only on the preventive costs of clinical mastitis and/or on specific breeds  and/or determinist methods with no variance associated with the mean losses were excluded. In the present work, production losses and curative extra costs were eligible to be considered as clinical mastitis losses. All expressions and proxies of the variance were accepted  and transformed into a unique unit  to compare the results across the included studies.The total number of identified publications and the applied two-step selection process for eligible studies, which was performed in accordance with the PRISMA guidelines , are illustrated in Y_DL  represents the mean clinical mastitis losses per animal in € in 2018, and i indicates the national currency of the respective country for which losses were determined in the year X. The nominal exchange rate (i into the currency € in 2002) and non-Eurozone . The index I_OCDEx includes the economic annual growth rate of the respective country and incorporates the inflation rate based on the consumer price index. The same procedure of standardization to the Euro (€) and the year 2018 was applied for all monetary values in the dataset  and the year 2018 for each respective country was performed as follows: dataset .I2 statistic, respectively  denoted the amount of heterogeneity that may not be explained through the inclusion of the factors in the meta-analysis. Publication bias was identified by performing the Egger test, a regression test for funnel plot asymmetry and inspection of the associated funnel plots  heterogeneity). Because the dataset contained moderators with different numbers of missing data, three subdatasets were proposed, as described in The meta-analyses were implemented in R  using the Metafor package . Random-ectively . For resel plots . Outlierp < 0.001). No publication bias was determined with the Egger test . The heterogeneity between the studies was very high .In total, 82 observations from nine studies were included in the meta-analysis . InfluenStaphylococcus aureus, coagulase-Staphylococcus, Streptococcus spp., and Escherichia coli, respectively . All other moderators, including the price of milk, were not associated with the losses of clinical mastitis . This result is in accordance with the fact that medium- or long-term SCCs arising from clinical mastitis are poorly accounted for in the present works since they are scarcely reported in the literature. Similarly, the way culling is integrated in the studies remains unclear. This result demonstrates that any economic reasoning focusing on the losses of mastitis is inappropriate and should be avoided. The present meta-analysis does not provide additional information since the economic reasoning in most of the raw models used in the present meta-analysis was biased. Recent literature on economics of udder health has increasingly focused on the strategic management of mastitis instead of its losses –21. ThisThe present work tried to explain the losses of clinical mastitis considering different groups of moderators . The modThe present work proposes an estimation of the losses of clinical mastitis for different etiologies. It failed to elucidate which contributor mainly influenced the losses of clinical mastitis and did not highlight any relationship between the price of milk or labor and losses due to clinical mastitis. This supports the avoidance any economic reasoning focused on the losses of each case of clinical mastitis since such a reasoning is inappropriate from an economic point of view. The internal and external validity of the losses evaluation is highly questionable in the case of clinical mastitis. These results also highlight the need for standardization on how economic assessments of losses due to animal diseases should be performed. This includes both methods and ways results are presented.All datasets generated for this study are included in the article/DR and TG designed and performed the analysis. BP, AF, PS, and GL contributed to the analysis, reviewed, and revised the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.