In this paper, an in-depth study of cross-media semantic matching and user adaptive satisfaction analysis model is carried out based on the convolutional neural network. Based on the existing convolutional neural network, this paper uses rich information. The spatial correlation of cross-media semantic matching further improves the classification accuracy of hyperspectral images and reduces the classification time under user adaptive satisfaction complexity. Aiming at the problem that it is difficult for the current hyperspectral image classification method based on convolutional neural network to capture the spatial pose characteristics of objects, the problem is that principal component analysis ignores some vital information when retaining a few components. This paper proposes a polymorphism based on extension Attribute Profile Feature (EMAP) Stereo Capsule Network Model for Hyperspectral Image Classification. To ensure the model has good generalization performance, a new remote sensing image Pan sharpening algorithm based on convolutional neural network is proposed, which increases the model's width to extract the feature information of the image and uses dilated instead of traditional convolution. The experimental results show that the algorithm has good generalization while ensuring self-adaptive satisfaction. With the popularity and development of the global network, data such as text, images, and videos from various social networking sites, news sites, and mobile apps are in explosive growth every day. Web data affects entire society on a large scale and profound level, influencing how countless people think, express, and behave. Unlike traditional structured and unstructured data, web data is represented in various forms such as text, images, audio, video, etc. These different types of media data obtained from other channels are combined to define comprehensive knowledge and describe objective facts in a new form with multiple perspectives and depth . This waLarge-scale multimedia data are usually strongly correlated in terms of high-level semantics. Cross-media data refers to those data that express semantically similar content but appear in different modalities, different sources, different contexts, etc. Cross-media data are usually characterized by cross-modality, cross-source, and cross-space. Cross-modality means that cross-media data express the same concept, the same semantics, or the same event through different modalities such as image, video, and text. Cross-source refers to cross-media data from other sources but expresses similar semantics . Cross-sThe paper on data dimensionality reduction using neural networks has stimulated the interest of a wide range of scholars. Later, it was gradually applied to the field of fault diagnosis by scholars, and mechanical fault detection based on deep learning aims to achieve a nonlinear relationship between the extracted features and faults by deep convolutional networks for autonomous extraction of depth-invariant features from the data . For croWith the rapid development of deep learning, modeling text data using deep neural networks becomes standard. The self-encoding algorithm is the first method applied to feature extraction of text data. The core idea of the self-encoding algorithm is to design the input and output of the neural network with the same number of neurons, use the input data as the output correction data, and use the low-dimensional vector of the intermediate layer to represent the feature vector of the text . An algoWith the rapid development of technology and the Internet, the accumulation of data will become unpredictable, and the visualization and analysis of the data after mining have become especially important. The potential correlations between data and the implied evolutionary laws can be seen . Data viXu et al. propose an integrated learning-based hash code learning strategy. Multiple “weak hash functions” and their weights can be learned, and the weighted Hamming distance across media can be calculated . The graConvolutional neural networks are composed of multiple two-dimensional planes; each consists of various neurons. Convolutional neural networks use local connections and shared weights to reduce the number of consequences, which effectively reduces the complexity of the network model. Based on these advantages, convolutional neural networks have many benefits in dealing with image problems, and many image processing models based on convolutional neural networks have been proposed. The results achieved by them are significantly improved compared with traditional algorithms. For example, Gogo Le Net is a famous classical network in image recognition, by using different sizes of convolutional kernels to change the perceptual field of the convolutional kernels in the web and finally stitching the features of different scales together to form the final one. The traditional neural network uses multiple nonlinear activation functions to extract features in layers by composite splicing, and the network automatically learns features during the training process, which reduces the workload of manual feature extraction to a certain extent. However, the standard neural network features are modeled as one-dimensional vectors, which destroy the spatial information of the features. For this reason, scholars have proposed convolutional neural networks. Convolutional neural networks introduce convolutional and pooling operations, which can fully extract images' local spatial semantic features. Pooling operations can expand the perceptual field and extract higher-level image features for image recognition. Convolutional neural networks usually consist of an input layer, a convolutional layer, a pooling layer, and a fully connected layer. The structure of a convolutional neural network is shown in k layer is defined as zik, the weight matrix is defined as wik, the bias is defined as bik, and the computational equation is given asThe convolutional layer plays a key influential role in the CNN network structure, enabling feature extraction from the input image. As the first step of CNN, it tries to reduce and capture helpful information by using the same kernel window to slide the whole picture to detect (convolve) specific image patterns to reduce the number of parameters . In addition, it uses multiple convolution kernels to extract image features in various ways . One of i feature map formed by the k convolution kernel of the layer is defined as Zik, the feature map created by the convolution kernel of k, the i − 1 coating is defined as Zi−1k the bias is defined as bik, and the preset downsampling style is down, and the computational equation is given asThe function of the pooling layer is to compress the input feature map and extract the main features, making the feature map smaller and simplifying the computational network complexity. This layer aims to remove irrelevant information and keep only relevant information. The input of this layer is the output of the nonlinear layer. The result of this layer is a simplified version of the input. The coating has pool cells organized topographically and connected to local regions in the information from the nonlinear layer. The pooling layer performs a form of nonlinear downsampling to reduce further the number of parameters, computations, and input noise. It divides the input into a set of nonoverlapping blocks and samples one element from each block, where the jl+1 denotes the output value of the first i neuron in the first l+1 layer; denotes the weight between the first i neuron in the wijll layer and the l+1 the first neuron in the coating jpjl(i) denotes the corresponding value of the neuron in the first l layer of the pooling operation; b denotes the bias. Many research works are devoted to semantic mining of single media and using it for media retrieval, characterized by retrieval results and samples of user queries belonging to the same media category, such as text retrieval. Since cross-media data have properties such as cross-modality and cross-platform, it is essential to analyze multimedia content and make reasonable use of the association relationship of data itself to obtain a deeper and more accurate understanding of cross-media content.In a fully connected layer (FCL), each neuron is connected to all neurons in the upper layer. Convolutional neural networks output targets as classes after multiple convolutions and pooling layers, so a fully connected layer is needed to map the output feature vectors to types. Deep typical correlation analysis considers the correlation between different media types and taps this correlation to a great extent through nonlinear mapping, but it does not consider the semantic information of various media types themselves, and directly ignoring the semantic information will lead to less precise intermedia relationships in the common subspace. The fully connected layer can expand the previous layer's output into a column vector that integrates local information with class distinction in the convolutional or pooling layer. This layer acts as a classifier in the whole convolutional neural network. This paper chooses the SoftMax classifier, a supervised learning algorithm with the primary category of input samples. The same selection cannot belong to more than one category simultaneously, suitable for solving multiple classification problems, where ∂The fully connected layers are in the network's last layers in the convolutional neural network structure. Each node of the fully connected layer is kept connected to the nodes of other network layers. This layer can significantly improve the parameter characteristics of the fully connected layer through convolutional operations. It also enables the integration of the local information of the pooling and convolutional layers. Softmax coating is used to identify and classify the output values of the fully connected layer by Softmax logistic regression, which maps each output value in the  range, i.e., the percentage of each value in the overall weight, to complete the classification of the output values. Softmax logistic regression is calculated as follows: the fully connected implicit layer is activated using a ReLU function that Softmax can convert the neurons of the last layer of the implicit layer:With the occurrence of overfitting, two standard pooling methods include average pooling and maximum pooling. Average pooling calculates the average value of an image region as the pooled value for that region. Complete pooling is to select the total value of the image region as the value after pooling for that region. For a 4 ∗ 4 feature map, after a filter with step size 2, size 2 ∗ 2, the core of the convolutional neural network is the convolutional layer, which generally consists of multiple convolutional kernels, and the number of output features of the convolutional layer is positively related to the number of convolutional kernels. The convolutional kernels are two-dimensional arrays, like a sliding window, that slide over the input image and perform mathematical operations on the local regions they pass through, respectively. The specific process is shown in Nth layer of the fully connected layer is connected to all neurons in the N − 1th layer, while the same layer is not connected. The fully connected layer can integrate the local information with category differentiation in the convolutional or pooling layers.In the CNN structure, after multiple convolutional and pooling layers, 1 one or more fully connected layers are connected. Each neuron in the K original class data and mapped to the semantic space corresponding to this K class. A standard mapping method is multivariate logistic regression to calculate the posterior probability distribution, generating a linear classifier that can be interpreted probabilistically. Logistics regression calculates the posterior probability x belonging to a class j by substituting the data x into the following logistics equation:Z=∑jexp(wjrx) is a normalization constant, r is the kind label, x is the feature vector of the input space, and w= . By this method, both text features and image features in different dimensions can be mapped to the same k dimensional semantic space, and cross-media semantic mining can be accomplished. Simply speaking, the multimedia objects of other modalities are mapped to a homogeneous semantic subspace by semantic learning of the extracted underlying features. Then the cross-media retrieval task is accomplished by matching the similarity based on semantic relatedness. For text documents T and images I, their corresponding feature spaces are labeled as Rrm×p and RIm×q, respectively, with m denoting the number of samples because text documents and ideas exist in pairs. Hence, their sample numbers are the same, p and q tell the dimensionality of text document and image features, respectively, SIm×k represents the isomorphic semantic subspaces corresponding to text documents and images after semantic mapping, RTm×q and, RIm×q represent the number of semantic categories, then, the semantic categories can be expressed as v=(v1 … vk). On the premise of obtaining the underlying feature space RTm×q, RIm×q semantic mapping is performed on text documents and images, respectively. The corresponding isomorphic semantic subspaces STm×k and SIm×k are independently trained from the underlying feature space. The same semantic concept base model generates both. The isomorphic semantic subspace obtained by training is the probability value of each sample belonging to its corresponding class, i.e., a probability distribution vector. This probability distribution vector can be derived from the above.The semantic matching algorithm (SM) does not consider the deep semantic connections between different types of media data. It completes the mapping of the joint space based only on the semantic relationships of their classes. Still taking text and image data as an example, image features and text features are extracted from the AP(q0) where AP(q0) denotes the average accuracy of the query term (q0) on the current search result page; n is the number of pages the user has turned backward. If the user turns the page to the first three pages, it means that the user has viewed the content of the previous 3 pages; then n is 3. log(n+1) is the conversion factor, which increases with the number of pages turned by the user and thus reflects the decrease in user satisfaction. In this paper, we ignore the user's direct page-turning behavior and consider the user's page-turning behavior to turn the page to the next search result page after browsing the current search result page. After extracting text and image features using deep learning methods, the method then uses deep semantic matching methods to complete the cross-media retrieval part, simply a three-layer fully connected neural network to map the previously extracted text features and image features to a K-dimensional space.In real search scenarios, users usually have page-turning behavior when dissatisfied with the current search results or want to get more results. In modeling user satisfaction with recent search results, the user's page-turning behavior is regarded as dissatisfaction with current search results. Then user satisfaction with the recent search results can be formally defined as To better estimate user satisfaction with the current search results, this paper considers a correlation between user satisfaction with the recent search results and user satisfaction with the previous query . SupposeThe cross-media semantic mining algorithm based on a convolutional neural network does not use the traditional feature extraction method in the image feature extraction stage. But train the convolutional neural network based on the images and tags in the Image Net database, and then fine-tune the web with relevant images. Finally, the retrieved images are used as the convolutional neural network's input, and the convolutional layer's output is calculated to select the appropriate intermediate layer for the image features. The components are first extracted once using the traditional TF-IDF or LDA method when extracting text features. Then it is used as the input of the fully connected neural network to train this network for feature extraction. After removing text and image features using deep learning methods, the method then uses deep semantic matching methods to complete the cross-media retrieval part . This meIn the traditional image classification and recognition, the image feature extraction is mainly done manually, and its image features specifically include image edge, image texture, image color, etc. According to the traditional image classification mechanism, the image features of the training set are first extracted; the essential elements complete the classifier's training. Then the test set images are classified by the classifier. A support vector machine (SVM) is one of the most widely used classifiers. In application practice, many external factors interfered with traditional image classification and recognition techniques, including background, light and shadow, etc., and require technicians with rich practical experience. Otherwise, they will significantly limit the image classification and recognition effect. In addition, cross-media semantic matching image features are relatively complex, creating great difficulty for technicians to build efficient image classification and recognition systems and to a certain extent limiting the improvement of the efficiency of image recognition. Based on the traditional image classification and recognition technology, the image classification technology based on the neural network does not need to rely on the subjective judgment of technicians. It achieves the extraction of image features with more detailed data, which can realize the robustness and completeness of image features . Among an = 299 × 299 = 89401. Therefore, the values of the input layer neurons are multiplied by the weight values, and bias values are added to obtain the importance of the hidden layer neurons. Subsequently, the hidden layer neuron values are passed through the activation function to form the input values of the neurons in the next layer. In a fully connected neural network, the weight parameters vary, and when the hidden layer contains 10000 several neurons, the scale of the hidden layer's weight parameters grows exponentially to 894011 × 0000 = 9 × 108. The fully connected neural network contains multiple hidden layers, which leads to a vast number of parameter values in the neural network model and poses a considerable challenge to network training.In the fully connected network model, if the input image is a regular 299 × 299, the size of the input layer neurons will be as large as Meanwhile, in the neural network's input layer, two-dimensional images are usually transformed into one-dimensional series, which prevents the information between pixels from being fully utilized. In addition, the number of network layers of fully connected neural networks is limited because the parameter size of fully secured neural networks is too large for network training to be carried out efficiently. However, due to its massive number of parameters, it consumes many computational resources. Therefore, the image classification technique based on the fully connected neural network has limitations in the application scope. The PR curves of each retrieval method on the database are shown in Deep typical correlation analysis considers the correlation between different media types and taps this correlation greatly through nonlinear mapping. Still, it does not feel the semantic information of various media types themselves, and directly ignoring the semantic information will lead to less accurate intermedia relationships in the common subspace. In this paper, we propose an improved deep typical correlation analysis algorithm, deep semantic typical correlation analysis (DSCCA), which maximizes the correlation of text and images using deep neural networks while ensuring semantic consistency across media types, specifically embodied by maximizing the similarity of samples in the same category for text features and image features (the variety here is not the category of media types). This is done by maximizing the similarity of samples in the same category (where the class is not the media type but the semantic category of data) and minimizing the similarity of models in different categories. The first method applied to text data feature extraction is the self-coding algorithm. The core idea of the self-coding algorithm is to design a neural network with the same number of neurons for the input and output, use the input data as the correction data for the production, and use the low-dimensional vector in the middle layer to represent the feature vector of the text. After extracting the semantic features, the Euclidean distance between pieces of the same class in images and texts is as tiny as possible. The Euclidean distance between representatives of different types is as considerable as possible in the new common subspace . On the Thus, the features of the cross-media semantic matching and user adaptive satisfaction classification mechanism based on the convolutional neural network include the following: First, based on the traditional image classification mechanism, the convolutional neural network relies more on big data to complete the image feature extraction, without the need for technicians to set up the feature extractor in advance, and the final extracted features have higher robustness. Second, compared with fully connected neural networks, the parameter size of convolutional neural networks is significantly reduced, which can avoid the overfitting problem and enhance the training depth of the network, thus improving the classification function of neural networks for images. Convolutional neural networks are introduced in this study to classify cross-media semantic matching and user adaptive satisfaction.Media information of different modalities has the same semantic meaning, which provides a strong guarantee for cross-media retrieval. For cross-media retrieval, it does not rely on the shape, size, sound, and other characteristics of media objects to identify whether they are like each other. Still, the semantic information is expressed by these media objects to determine their similarity, and the challenge here is how to unify the media data of different modalities to the same semantic level, which is not available in traditional retrieval techniques . Here itThe merit of cross-media retrieval results can be judged based on these two performance evaluation metrics. A higher precision rate indicates that the algorithm is more capable of querying out relevant semantic multimedia; a higher completeness rate suggests that the algorithm is less likely to query out irrelevant semantic multimedia objects. If the relevance of the query result to the query sample is greater than the set threshold, it is considered that it should be retrieved. With “precision rate” as the vertical coordinate and “recall rate” as the horizontal coordinate, the precision rate and recall rate under different thresholds are calculated from large to small, and the corresponding curve is the PR curve by smoothly connecting these points, which can more comprehensively represent the retrieval efficiency. In general, if the PR curve of an algorithm is higher than the PR curve of another algorithm, or if the corresponding area is more extensive, we consider that the algorithm has better performance. Logistic stiff regression method, which is used to achieve cross-media semantic mining, extracts LR features of different types of media data and proposes a combined semantic space in combination with the deep semantic typical correlation analysis in Chapter 3, and introduces the solution method of integrated features. This combined feature contains both intermedia-related semantic information and intramedia semantic information. Unlike traditional structured and unstructured data, web data is represented in various forms such as text, images, audio, video, etc. These different types of media data obtained from other channels are combined to define comprehensive knowledge and describe objective facts in a new form with multiple perspectives and depths. And it can mine higher-level semantic information from cross-media data than the deep semantic typical correlation analysis algorithm. The experimental part compress the algorithm with four other mainstream cross-media retrieval algorithms on two standard cross-media datasets. It verifies the stability and effectiveness of the algorithm from different perspectives in the form of graphs. The MAP values for different retrieval categories on the dataset are shown in n DCG@10 metrics. On the other hand, it increases as the size of the dataset increases. This indicates that the query recommendation model in this paper is less affected by the scale of experimental data.The experimental results show that this paper's adaptive query recommendation model outperforms the benchmark model (QFG) significantly after the first day. And the model proposed in this paper performs stably with less fluctuation in n DCG@10 evaluation metrics, and the possible reasons for this are as follows: (1) This model can select more query terms as candidate recommendation terms by exact matching and fuzzy matching, which enriches the set of candidate query terms. (2) This model can more accurately recommend candidate query terms for users by mining user search logs and integrating user satisfaction with current search results. The results of the AQR and QFG experiments are shown in In contrast, the query flow graph model is more affected by the scale of the observed dataset and sensitive to the scale of the experimental dataset. Large-scale multimedia data are usually strongly correlated in high-level semantics. Cross-media data express semantically similar content but appear in different modalities, sources, contexts, etc. It is possible that as the data increases, the performance of the adaptive query recommendation model in this paper will converge with that of the query flow graph model and remain stable (this hypothesis was not verified in this experiment due to the lack of sufficient data). The adaptive query recommendation model (AQR) outperforms the query flow graph model (QFG) in Cross-media semantic matching and user adaptive satisfaction analysis algorithms improve the loss function of deep typical correlation analysis. Media label information is introduced to maximize the similarity of same-category samples  and minimize the similarity of different-category models maximum relevance. After extracting semantic features, the Euclidean distance sum of same-category pieces in images and text is as small as possible, and the Euclidean distance of different-category elements is as considerable as possible under the new common subspace. It is ensured that the learned public subspace reflects both intermedia correlation and semantic information. Finally, the effectiveness of the DSCCA algorithm is verified by doing cross-media retrieval experiments on the Wikipedia and XMedia datasets.The advent of the Internet era and the rapid development of multimedia information technology have brought the convenience of information sharing and posed challenges to multimedia information services; better semantic matching and user satisfaction of multimedia information have become the main direction of research today. Cross-media semantic matching and user adaptive satisfaction are based on semantic matching of different modalities. The early cross-media semantic matching techniques focus on analyzing the underlying features of multimedia information and ignore the connection between the underlying elements and the high-level semantics of multimedia information. Analyze the association between the underlying components and the high-level semantics of multimedia information. Construct a homogeneous high-level semantic space based on the underlying feature space of different modal objects, and apply the integrated learning approach to cross-media semantic matching. The Bagging-SM approach is proposed to match the semantics of multimedia objects of other modalities. The cross-media semantic information mined by the semantic typical correlation analysis algorithm of the convolutional neural network is only at the level of similarity. Dissimilarity does not contain high-level semantic information. Therefore, this paper constructs a combined semantic space to mine higher-level semantic information. Logistic Stick regression classifies text and images separately. The classification results are first used as the respective semantic features, i.e., LR features. The extracted semantic features relate to the improved deep semantic typical correlation analysis to extract DSCCA features as the combined features. Based on the conclusions from user experiments, this paper innovatively proposes an adaptive query recommendation model that incorporates users' current semantic matching states. Under different semantic matching satisfaction states of users, other strategies should recommend matching more novel or enhanced relevant information representations for users.


Clearance of protein waste products from the brain is accomplished by a combination of advection and diffusion in cerebrospinal fluid (CSF) and interstitial fluid (ISF). In the glymphatic model, there is a flow of ISF in the interstitial space, and both advection and diffusion occur there. Such a flow of ISF would be slow and difficult to detect directly, and its existence has proved controversial. Waste clearance has been shown to occur mainly during sleep, during which the volume of the interstitial space increases substantially due to ISF emitted from astrocytes. Here I show that this volume increase of the interstitial space, by itself, should lead to a slight reduction of diffusive transport, due to dilution of the waste solute, but to a significant increase in flow rate and advective transport, due to lowered hydraulic resistance. Thus, a flow of ISF together with the observed volume increase of the interstitial space might provide an important mechanism contributing to the enhanced clearance during sleep. Nearly all neurodegenerative diseases are linked to abnormal accumulations of metabolic waste products. For example, proteins such as amyloid-beta and tau form toxic aggregates that have been linked to Alzheimer’s disease. Removal of metabolic waste from the brain takes place within the interstitial fluid (ISF) and cerebrospinal fluid (CSF) by a combination of advection and diffusion. According to the glymphatic model , 2, the Detecting a flow of ISF within the interstitial space presents a significant experimental challenge. The experiments must be done in vivo. A flow can only be detected after the introduction of tracers, but invasive procedures significantly suppress glymphatic function . The detHere I present a theoretical argument in favor of the existence of a flow of ISF and advective transport in the interstitial space, based on the experimental evidence that the waste clearance occurs primarily during sleep, during which the volume of the interstitial space is increased substantially by fluid expelled from astrocytes .Xie et al.  found tha et al.  found thll, e.g. –13. The ll, e.g.  found thporosityC in the interstitial space scales as C decreases from wake to sleep due to dilution.Consider a control volume D is the free diffusivity. The concentration gradient C/L, where L is the typical distance between arterioles and venules. This distance L does not change from wake to sleep, so the concentration gradient scales as C in going from wake to sleep. The diffusive flux then scales asS with unit normal vector S over which the flux actually occurs, which increases as the porosity increases. (Here S may be considered as the outer surface of the PVS into which the solute is diffusing.) This portion of S scales as the cross-sectional area of the interstitial space, and hence as The diffusive flux rate (transport per unit area per unit time) is given by Fick’s first law, mentclass2pt{minimp is the pressure, S isS over which the flux actually occurs, which increases as Now let us consider the effect of increased porosity on the advective flux rate of the solute due to a presumed flow of ISF in the interstitial space. The advective flux rate is Fig. t}FD eq.  and the t}FA eq.  as functe et al.  developeXie et al.  found thHere I have examined the effect of just one of several possible wake-sleep changes on waste clearance from the brain: the increase in porosity of the interstitium. Other changes might also affect the clearance rate. For example, an increase in the size of PVSs during sleep would lower their hydraulic resistance and thus promote a greater overall flow rate along them. Perhaps the driving mechanism for PVS flow is stronger during sleep. However, increasing the flow of CSF along PVSs will not, by itself, enhance overall clearance significantly if diffusion is the only transport process within the interstitial space. A faster flow along the PVSs might establish a slightly lower concentration within them, thereby slightly increasing the concentration gradients driving diffusion in the interstitial space, but this slow diffusive process will still govern the overall clearance rate, and the ability to speed up clearance in this way is quite limited. On the other hand, a large wake-sleep increase in clearance rate would be much more easily generated by the significant increase in flow rate in the interstitial space that would occur because of increased porosity and lowered hydraulic resistance there.The hydraulic resistance of the interstitial space is inversely proportional to its permeabilty, which is a poorly known quantity: estimates of its value in the mouse brain span more than two orders of magnitude , 18, andl et al.  applied l et al. . In futul et al. . Althoug


Soil moisture has been considered a key variable in governing the terrestrial ecosystem. However, it is challenging to preserve indigenous soil characteristics using conventional soil moisture monitoring methods that require maximum soil contacts. To overcome this issue, we developed a non-destructive method of evaluating soil moisture using a contactless ultrasonic system. This system was designed to measure leaky Rayleigh waves at the air–soil joint-half space. The influences of soil moisture on leaky Rayleigh waves were explored under sand, silt, and clay in a controlled experimental design. Our results showed that there were strong relationships between the energy and amplitude of leaky Rayleigh waves and soil moisture for all three soil cases. These results can be explained by reduced soil strengths during evaporation processes for coarse soil particles as opposed to fine soil particles. To evaluate soil moisture based on the dynamic parameters and wave properties obtained from the observed leaky Rayleigh waves, we used the random forest model. The accuracy of predicted soil moisture was exceptional for test data sets under all soil types (R It has long been recognized that soil moisture is a key variable in terrestrial ecosystems. Despite the amount of water held by the soil being a small fraction in the Earth’s system when compared with other hydrological components, the accurate estimates of soil moisture are of great importance to hydrological, agricultural, biogeochemical, and climate processes ,2,3. Soip to 90%  and is ap to 90% . That isThere have been various techniques developed to estimate soil moisture for laboratory and in situ experiments. Classical methods are to use thermo-gravimetric analysis (oven drying) and calcium carbide reaction with water in the soil ,14. AlthSeveral efforts have been made for addressing the major two issues described above . One of the techniques to overcome these shortcomings is to utilize radioactive techniques, such as neutron scattering and gamma attenuation ,19. GrouThe air-coupled ultrasonic method has been introduced as a promising non-destructive technique, where the testing configuration is fully contactless over the object . The ultAn overview of the proposed nondestructive technique to estimate soil moisture using leaky Rayleigh waves is illustrated in k is a wavenumber of the Rayleigh wave, and q and s are the longitudinal and transverse vertical wavenumbers, respectively. As the density ratio, In a joint-half space, such as fluid and solid, a portion of the Rayleigh waves in the solid is leaked into the fluid, called a leaky Rayleigh wave . The leaigh wave .The Rayleigh wave is a mechanical wave that propagates along the surface of a medium. The wave motion is elliptical, including a portion of P- and S-waves, where the dilatational and distortional motion is guided at the surface boundary. Therefore, the propagation of the Rayleigh wave is governed by characteristics of both P- and S-waves. In particular, the distortional motion is the unique behavior of the solid due to shear resistance. Recent studies show that the degree of Rayleigh wave propagation well correlates with the phase change of cementitious materials ,34. ThisTo measure the leakage of the Rayleigh wave propagating at the interface between the air and soil, an air-coupled testing configuration is required. The test set-up includes a contactless ultrasonic transducer, receiver, and acoustic barrier as described in The contactless ultrasonic system was carefully designed to measure leaky Rayleigh waves without touching the surface of the soil. As shown in The transmitter and receivers were attached in a 3D-printed frame and positioned apart from the soil specimen without disturbances. An acoustic barrier was placed between the transmitter and receivers. The barrier was composed of several layers of plastic 3D-printed materials and an air gap, which caused the delay and attenuation of the direct waves due to the acoustic mismatch . That isThree primary soil types  were used in this study as presented in The experimental design for the contactless ultrasonic transmitter and receiver to estimate soil moisture based on leaky Rayleigh waves was presented in To explore the relationships between soil moisture and Rayleigh waves, the prepared soil specimens were saturated and then dried in a temperature–humidity-controlled chamber during experiments. There might be unintended and undiscovered drying effects on Rayleigh waves to analyze soil moisture due to the hysteretic effects of water filling and drying the soil. However, for the sake of experimental simplicity, we assumed that these effects were relatively negligible compared to the impacts of soil moisture on the leaky Rayleigh waves. A drying cake method was used to reduce the friction between the soil and the acrylic box, which entailed coating the four walls and bottom of the box with a thin layer of Vaseline . In addiIn this study, we used random forest regression due to its versatility to estimate soil moisture with the different dynamic parameters of leaky Rayleigh waves measured on the surface. Instead of developing mechanistic models that could explain causal relationships between the soil moisture and leaky Rayleigh waves, we applied this machine learning-based approach to the waves to overcome the limited nature of previous research on this topic. In addition, the purpose of this study was not to explore their relationships but to explore the potential of leaky Rayleigh waves to estimate soil moisture without soil disturbances. The random forest used in this study was originally proposed by  and has N sub-datasets are sampled from the original dataset, which is trained with N sub-models. All the trained N models are combined into one final model [Random forest regression utilizes the bootstrap aggregation (bagging) of regression trees. With this algorithm, al model . The useal model . HoweverThe excitations of the Rayleigh wave only require a free and viable surface, but only a joint half-space medium could generate an undistributed surface wave pulse . This waWhich incident angle should be used for each soil type? To explore this question, we first identified the arrival time of the direct acoustics for the incident angle experiments , amplitude, energy, and phase. TOF is a fundamental characteristic of dynamic response and is easily identified from the experiment. The direct comparison between soil moisture and TOF is described in ectively b,c. The ectively a. InsteaAs discussed above, the TOF for leaky Rayleigh waves was not a reliable indicator to predict soil moisture. We instead explored the relationships between soil moisture and normalized energy, amplitude, phase, and period during the signal arrivals from 0 o 750 μs . These pmplitude , but notThe distance between MEMS provided the different arrival times of leaky Rayleigh waves and direct acoustics and thus their unique characteristics. That is, the developed sensor array (eight MEMS channels) had the ability to provide imperative information on wave characteristics and properties to assess soil moisture, which could not be obtained by a single receiver. The analysis of leaky Rayleigh waves measured by a single receiver for monitoring soil moisture could lead to biased and misleading results as shown in To overcome the challenge of estimating soil moisture based on the dynamic parameters only, we applied random forest to the dynamic parameters and wave properties of leaky Rayleigh waves . We usedSoil moisture is a key element in controlling the exchange of water and energy between the land surface and the atmosphere and therefore terrestrial environments. There has been growing interest in the development of soil moisture sensors to improve underlying techniques and tools ,59,60,61Although we conducted identical experiments in a controlled chamber, distinct wave patterns emerged from sand compared to silt and clay. For the case of sand, the relationships between the dynamic parameters and wave properties of leaky Rayleigh waves and soil moisture were relatively low, especially when compared with cases for silt and clay. During the experiments, we noted that the volumetric shrinkage enlarged gaps between the soils, especially for silt and clay but not for sand. To minimize the gaps, we applied Vaseline to the walls of the acrylic boxes to reduce friction between the soil and the boxes , leadingOne concern was that the dataset and experimental designs we used in this study were admittedly limited. Natural soils were inherently heterogeneous, leading to imperative but complex soil moisture dynamics e.g., ,69,70). ,70. 69,7Beyond what we demonstrated in this study, three possible extensions related to the motion of the Rayleigh waves are listed as follows. First, the initiations of TOF variations should be further investigated for soil properties, such as wilting points. Certain time points were discovered from both silt and clay with different soil moisture contents. Therefore, the different soil types could be investigated to define the physical meaning of the initiations. Second, the shear modulus of soil could be estimated and evaluated through the dynamic response based on leaky Rayleigh waves. With the volume and weight of soil specimens during experiments, the shear modulus could be analyzed to develop a rheological model to explore their underlying mechanistic explanations. Noted that changes in the volume and weight were not critical parameters to estimate soil moisture in this study. Third, further experiments should be conducted to increase the effective depths of the proposed method by modifying the frequency of the ultrasonic waves. Fourth, the long-term fluctuation of soil moisture could be monitored with a fully automated system without soil disturbances. In this context, the proposed method could be further integrated with unmanned units in the field. With this integration, fully automated measurements could be achieved with minimal human intervention in soil environments.The ultrasonic system developed in this study was able to clearly detect the changes in surface soil moisture through the excitation and reception of leaky Rayleigh waves.Strong relationships were identified between the energy and amplitude of leaky Rayleigh waves and soil moisture for the three tested soil types .The proposed deep learning approach showed the accurate and robust estimations of soil moisture using the dynamic parameters and wave properties of the observed leaky Rayleigh waves for the validation and test datasets, regardless of the soil texture and soil moisture contents (RThis study demonstrated the potential of using leaky Rayleigh waves to continuously estimate surface soil moisture without any soil disturbances through the development of a contactless ultrasonic system. Based on the findings of this study, the following conclusions were drawn:


In this work, a characterization study of the interfacial interaction between different types of graphene nanoplatelets and an epoxy matrix is computationally performed. To quantify the discrete mutual graphene–epoxy “interfacial interaction energy” (IIE) within the nanocomposite, molecular dynamics simulations with a reactive force field are performed on a localized model of the suggested nanocomposite. Pull-out molecular dynamics simulations are also performed to predict the interfacial shear strength between the two constituents. The results indicate a significant increase in interfacial adhesion of functionalized nanoplatelets with the hosting epoxy matrix relative to virgin graphene nanoplatelets. The obtained results also demonstrate a dramatic increase in the interfacial interaction energy (IIE) (up to 570.0%) of the functionalized graphene/epoxy nanocomposites relative to the unmodified graphene/epoxy nanocomposites. In the same context, the surface functionalization of graphene nanoplatelets with the polymer matrix leads to a significant increase in the interfacial shear strength (ISS) (up to 750 times). The reported findings in this paper are essential and critical to producing the next generation of lightweight and ultra-strong polymer-based nanocomposite structural materials. The development of aerospace structural materials has been going through a determined research effort for the last two decades ,2,3. IncGraphene nanoplatelets (GNP) are widely utilized to reinforce polymer structures used in many engineering applications because of their remarkable mechanical, thermal, and electrical properties. The synthesizing process of GNP/epoxy nanocomposite materials requires professionality and a deep understanding of their molecular structure, especially when they are classified as advanced composites used in manufacturing aerospace and aeronautics structural components. Dealing with a wide range of key processing parameters in these nanocomposites, such as the type and curing degree of the epoxy resins, in addition to the size, concentration, and dispersion of the GNPs, is essential to optimize the mechanical properties and integrity of the bulk nanocomposite. The mutual interfacial interaction energy (IIE) is one of the most important factors used in determining the load transfer between nanocomposite constituents at the nanoscale. Fortunately, polymer matrices have shown a natural affinity to the reinforcements of carbon allotropes, including GNP. What is more, chemical modification  of the GNP surface can further improve the mutual affinity with the hosting matrix ,16,17.2 > -COOH > -OH > -F > -CH3. Finally, the study was concluded by specifying several factors which affect the mutual interaction energy between the constituents in the nanocomposite. These factors were identified by the used functional group types or their chemical structure, concentration, polarity, and their distribution on the nanoplatelet surface. However, the mutual shear stress level between the functionalized graphene nanoplatelets and polymer matrices was affected by another factor, which is referred to as the mechanical interlocking between them.The pull-out energy can be utilized to determine the shear strength or interfacial adhesion between two surfaces. Due to the difficulty and limitations of using standard experimental tools to fully access the pull-out energy, a reliable molecular dynamics (MD) computational tool called “pull-out MD” (POMD) simulations has shown its validity in addressing the interfacial shear strength (ISS) between nano-reinforcements (such as GNP and carbon nanotubes) and the hosting polymer matrix ,20,21,226H13, and -C2H4(C2H5)2. The results indicated that the largest functional group type of -C6H13 produced the highest interfacial bonding energy between the constituents. This is because they were relatively well-embedded within the polymer matrix. However, the complex configuration of the -C2H4(C2H5)2 functional groups provided better mechanical interlocking, and, thus, the strongest shear force. This concludes that the size and molecular configuration of the functional group play an important role in controlling the interlocking mechanism and interfacial load transfer.In another study, Lv et al.  investig2, -CH3. The reactive force field (ReaxFF), developed by Chenoweth et al. [2 functional groups can produce higher electrostatic (Coulomb) energy relative to the -H and -CH3 functional groups, which are non-polar. Due to their high polarity, the epoxide -O- groups provided the highest level of interfacial binding energy. Thus, the epoxide -O- groups exhibited a traction effect on the PE chains that was comparable to the -CH3 functional groups, regardless of their poor interfacial binding.The POMD simulations were utilized by Jin et al.  to exploh et al. , was useThe use of reactive force fields is critical for most MD simulations, and it could have benefits over using fixed-bond force fields, as it can efficiently capture the interactions of the intermolecular covalent bonding process occurring at the interface between the matrix molecules and the reactive functional groups in the nanofillers. In this context, Hou et al.  employedWith the capabilities of POMD simulations to capture the characteristics of interfacial regions between nanofillers and polymer matrices, which are briefly presented above in addition to other relevant computational results in the literature ,32,33,342 and Amide -C(=O)-NH2) to represent the FGO/epoxy nanocomposite. The chemical compositions of these two case studies were modeled based on the data presented by Park et al. [This work utilizes MD samples for the interphase region of GNP/epoxy nanocomposites, which were previously modeled by Al Mahmud et al. . Three ik et al. .Five replicates of the MD models were established for every case study to consider the variation probability in the predicted mechanical properties. Representative models of the three nanoplatelet/epoxy  systems are shown in The atomistic data used in establishing the MD models are given in Here is addressed two important correlative interfacial characteristics at the nanoplatelet/epoxy interphase region, which are investigated in two separate subsections. First, the prediction of the IIE between GNP and epoxy is investigated and discussed. The prediction of the ISS between GNP and epoxy is subsequently discussed.NP), epoxy (PEepo), and the full nanocomposite MD model (PEMD) [The nanoplatelets/epoxy interfacial binding can be assessed using the IIE. The IIE for a nanoplatelet/epoxy nanocomposite can be estimated using Equation (1) using the potential energies of the nanoplatelets (PEl (PEMD) ,41:IIE =The levels of the estimated IIE for the GNP/epoxy, GO/epoxy, and FGO/epoxy systems are shown in As the IIE involves different types of energy terms, which are established depending on the molecular system type and configuration, an analysis of the contribution percentages of the energy terms in the predicted IIE for each nanocomposite system is shown in 3 to sp2 ratio  behavior of the applied pulling force whose profile is shown in pull depends only on one variable (tstep) and two constant values , it is highly critical to specify the optimum values for these three terms. To clarify that, the increment in tstep has to be as small as possible to capture the threshold value of the pulling force in addition to being able to distinguish the stages in the obtained slipping profile. The total time required for the simulation process  has to be as large as possible to satisfy the nanoplatelet slipping onset point and securely obtaining the entire slipping stage in the simulation process. To satisfy all of the above requirements, specifying the optimum value of Fmax has to be somehow compatible with the other terms in Equation (2). An additional hidden term has to be considered in Equation (2) represented by the computational cost, which depends on the available computational resources.The pulling process was proposed to be performed in a very steady slow manner. This is necessary to capture the critical value of the pulling force at which the relative interfacial slipping onset occurs between the nanoplatelet and the epoxy. Thus, the variable of the applied force was derived to be gradually increased with time in the MD simulation, which is obtained by using Equation (2):Two important aspects were considered in the simulation process of all the MD models. First, it is necessary to activate the periodic boundary condition settings for the MD models to mimic a real-sized system. Second, both the zigzag and armchair directions in the graphene structure, which are illustrated in The following results were obtained after performing the pull-out simulations for the three nanoplatelet/epoxy systems. For a representative GNP/epoxy MD model, a displacement profile of the GNP as a function of the pulling force on at each carbon atom in the GNP lattice is shown in −3 Kcal/mol-Å per carbon atom. On the other hand, the corresponding GNP displacement value was estimated to be around 25 Å, yet it was around 5 Å at the beginning of stick–slip behavior. Note that any of the two threshold values was obtained by estimating its average value over the MD replicate models and considering the two directional pulling axes in each replicate.Once the GNP starts its slipping after the initial sticking stage, pulling the GNP along the armchair direction demonstrates prominent stick-slip stages which form a stair-like profile. In contrast, a very short sticking stages can be observed once the GNP begins to slip along the zigzag axis. The relative difference in the stick–slip behavior is associated with the contrast in the slipping path along each of the two axes, which is governed by the energy surface topology of the aromatic lattice structure ,44,45. TTo assess the interfacial binding in the functionalized graphene nanoplatelet/epoxy systems, a quite similar MD simulation procedure was adopted. The results were also obtained and analyzed to be compared with the predictions above. As functionalized nanoplatelets exhibit a wrinkled structure and rough topology, in addition to the produced interfacial covalent and/or noncovalent bonding with the hosting epoxy matrix, both the GO and FGO were expected to yield larger pull-out resistance levels in comparison to that observed in the GNP/epoxy system. For the GO/epoxy system, From the visual perspective, For the FGO nanoplatelet shown in 10) of the applied pulling force per carbon in the nanoplatelet lattice and parallel to the two directional axes. Once again, the particular different behavior in the GNP slipping profile is recognized at the initial stick stage, which is highly sensitive to the pulling direction. In contrast, all the slipping profiles of GO and FGO are closely analogous by using the logarithmic scale and demonstrate less sensitivity to the pulling direction. More importantly, both the GO and FGO require larger magnitudes of the applied pulling force to fulfill their slipping onset, which is relatively much lower (in the range of two to three orders of magnitude) for the GNP. The nearly identical mechanical behavior observed in the slipping of the GO and FGO ensures that the nanoplatelet-matrix ISS is likely to be heavily influenced by the interlocking between the two constituents, which is established by the wrinkled and rough surfaces of the functionalized nanoplatelets.To accomplish analyzing the difference in the pulling response between the three nanoplatelet types, Given that ISS is the pulling force threshold registered at the onset of slipping, In addition to its importance in estimating the ISS, the pulling force threshold value is essential to assess the static interfacial friction between the nanoplatelet and the hosting epoxy matrix. The ISS can be basically determined by multiplying the overall applied pulling force threshold value averaged over the MD replicates of the nanocomposite system by the number of carbon atoms in the nanoplatelet. Thus, as the graphene nanoplatelet lattice was modeled with 836 carbon atoms, the ISS in the GNP/epoxy system is estimated to be about 0.4 kcal/mole-Å. This is significantly less than the ISS in the GO/epoxy system, where its average value was estimated to be about 250 kcal/mole-Å. The FGO/epoxy system, however, surpasses the other nanocomposite systems by registering the highest ISS with an average value estimated to be about 300 kcal/mole-Å. In other words, the surface functionalization of the GNP resulted in a significant enhancement in the produced ISS in the GO/epoxy and FGO/epoxy systems over the GNP/epoxy system by about 625 and 750 times, respectively. The surface functionalization of the GNP resulted in a tremendous enhancement in the produced ISS in the GO/epoxy and FGO/epoxy systems over the GNP/epoxy system, by about 625 and 750 times, respectively. Due to the covalent interfacial bonding involving the highly reactive amine/amide functional groups in the FGO and the hosting epoxy, the ISS of the FGO/epoxy system is larger than that of the GO/epoxy system by about 20%.Based on the ISS values, which are estimated for the three nanocomposite systems, it is clear that the functionalization of graphene nanoplatelets can substantially enhance adhesion at the nanoplatelet–matrix interface. Thus, the bulk material strength and toughness are implicitly promoted. The predicted results confirm that the degree of improvement in the material ISS is highly reliant on the composition and quantity of the functional groups. This can be logically recognized by the additional improvement in the ISS of the FGO/epoxy system over the GO/epoxy system, which is attributed to the interfacial covalent bonds established between the amine/amide functional groups and the epoxy matrix. In general, producing a nanocomposite with a robust molecular structure is governed by establishing an interface with covalent and noncovalent bonding. The interface interlocking is stimulated by the wrinkled and rough topology of the GO and FGO, and results in improvement of the material robustness. These factors qualify the localized interphase regions of the functionalized nanoplatelet/epoxy to serve as strong toughening zones within the bulk composite material.This computational study utilizes experimentally informed nanocomposite MD models to address the effect of surface functionalization on the graphene/epoxy interfacial binding using POMD simulations. It has been established that introducing functional groups to the graphene surface can significantly improve both IIE and the ISS for the simulated nanocomposites. The outcomes from the simulated nanocomposite MD models refer to the merit of using GO rather than GNP reinforcements which is demonstrated by increasing the IIE up to 215% and increasing the IIE up to 625 times in the nanocomposite. This significant promotion in the material interfacial properties was primarily originated due to the high concentration of oxygen groups bonded to the graphene nanoplatelet. Substituting some of the oxygen functional groups with highly reactive amine and amide functional groups resulted in further increasing the IIE up to 570% and the ISS up to 750 times in FGO/epoxy relative to GNP/epoxy system. Such reactive functional groups have been found to promote the nanoplatelet-matrix interfacial adhesion by establishing supplementary covalent and noncovalent interaction energies. Other additional factors represented by the nanoplatelets wrinkling and rough topology of GO and FGO have been found to enhance the interference between the functionalized nanoplatelets and the hosting matrix. This, in turn, promotes the interlocking mechanism between the two constituents at the interphase region. Consequently, locally promoting the nanoplatelet-matrix interfacial binding is implicitly essential in establishing the bulk-level strength and toughness of the nanocomposite material.


Spectrum-based Fault localization have proven to be useful in the process of software testing and debugging. However, how to improve the effectiveness of software fault localization has always been a research hot spot in the field of software engineering. Dynamic slicing can extract program dependencies under certain conditions. Thus, this technology is expected to benefit for locating fault.We propose an improved dynamic slicing for spectrum-based fault localization under a general framework. We first obtain the dynamic slice of program execution. Secondly, we construct a mixed slice spectrum matrix from the dynamic slice of each test case and the corresponding test results. Finally, we compute the suspiciousness value of each statement in the mixed-slice spectram matrix.To verify the performance of our method, we conduct an empirical study on 15 widely used open-source programs. Experimental results show that our approach achieves significant improvement than the compared techniques.Our approach can reduce approximately 1% to 17.79% of the average cost of code examined significantly. Where S is the node set of CFG, E is the set of edges, and edges represent the data dependency or control dependency between two nodes.System Dependence Graph: System Dependence Graph (SDG), SDG = {GPDGs, EInterpro} of multi-process program P is a directed graph. Where GPDGs is the PDG set, and each process is represented as a PDG; EInterpro is the set of inter-procedure calling edges and dependent edges. Data-dependent edges represent data flow between actual and formal parameters.The Static Slicing: Given a slicing criterion s is the interesting points in the program and V is the set of variables. A static slice of a program P is the set of all statements affecting the set of variables V in a point of interest s (a statement or block of statements).The Dynamic Slicing: Given a slicing criterion I is the input to the program, the s is the interest in the program, and V is the set of variables. Based on the input I, a dynamic slice is the set of all statements of the program P that affect the variable set V in the point of interest s (a statement or block of statements) in this execution.The concept of program slicing was first proposed by Weiser in his doctoral dissertation in 1979 . SubsequTo solve this issue, Dataflow-based program slicing algorithm. The basic idea is to first calculate the control dependencies in the program, and then according to the data flow transfer in the control flow analysis program, iteratively construct slices by calculating the statements of the direct and indirect dependencies of the variables. The first program slice proposed by Graphreach-based program slicing algorithm. The basic idea is to obtain program slices by traversing the constructed Program Dependency Graph (PDG) or System Dependency Graph (SDG), and finding other nodes that the program can reach from the slicing criterion according to the dependencies. For example, At present, the typical program slicing algorithm is based on the data flow equation and the graph reach ability algorithm, and other algorithms are extended or improved from these two types of algorithms. The following briefly introduces the program slicing algorithm based on data flow and the program slicing algorithm based on graph reach ability.By program slicing, all statements can be classified into correct set of statements and suspicious set of statements. Therefore, most of the slicing-based fault location techniques use different slices , executiSoftware fault localization has a problem that the search domain is too large and the accuracy is not high. Fault localization approaches often use dynamic slicing techniques to narrow down the scope of the fault. However, the existing forward computing dynamic slicing approaches  calculatFor the example program in arantula  approacharantula  and imprHowever, the existing forward-calculated dynamic slice side  calculatAs can be seen from columns 2 and 3 of In this section, we present an improved dynamic slicing for fault localization. Our approach collects the dynamic slice information of program execution. Next, we construct a mixed slice spectrum matrix from the dynamic slice information of each test case execution and the corresponding test results. Finally, we compute the suspiciousness ranking of each statement in the mixed slice spectrum matrix. The framework of our approach is shown in Dynamic slicing is an effective debugging technique, which has been widely used in fault localization. When program execution results in a fault output or exception, the debugger usually wants to quickly ascertain the range of possible fault statements. By calculating the backward dynamic slice of the fault output statement, the dynamic slice can narrow the range of fault localization. Compared with the backward calculation, after the occurrence of the fault, the forward approach can generate a dynamic slice, which is according to the direct dynamic dependency of bug output statement. The forward approach does not need to traverse the dynamic dependency in the back, so it can calculate the dynamic slice result more quickly when a program runs incorrectly.Dynamic slicing technology  reduces Therefore, the forward calculation approach is used to calculate the program’s dynamic slicing, and the influence set analysis of the defined variables is carried out to improve the slicing accuracy, and the obtained dynamic slicing results are used for fault localization. The forward calculation method obtains the dynamic slice of interest points in the program through the direct dynamic dependencies of the interest points in the program. This method needs to maintain the slice results of each execution instance of the program . The forDefinition1. Influence set Influence[v]. For a given statement s, the variable Influence[v]= {v′| The v′ is the variabl e actually referenced when v is defined, Def[s] is the set of variables defined in statement s, and the reference set Use[s] is the set of variables referenced in statement s.In this section, the influence set analysis is performed on the defined variables to remove irrelevant variables in order to reduce the redundancy of the slicing results.Definition2. Hybrid Slicing Spectrum Matrix (HSSM). For a program P consisting of n statements s and a given m test cases, each execution of the test case yields a dynamic slice (slice) and the corresponding execution result. The mixed slice spectrum matrix is a two-dimensional matrix composed of multiple dynamic slices and execution results.This hybrid slice spectrum matrix uses the program dynamic slice information to extend the program coverage information and combines it with the corresponding program execution results to generate the hybrid slice spectrum matrix.call), dynamic control dependency of slicePred). The final output is the statement s refers to the dynamic slice of the variable use and the statement s defines the dynamic slice of the variable def. Lines 1–3 calculate the dynamic slice[use] of the variable use in the reference set Use[s] of statement s. slice[use] of the referenced variable based on the dynamic data dependency of the current execution instance def according to definition 1: Influence[def]. Lines 11–18 compute the dynamic slice[def] of the variable def in the definition set of Def[s]. Line 11 executes the dynamic control dependency slicePred of the control dependent predicate pred. Lines 12–18 calculate the dynamic slice slice[def] of the defined variable, which is consist of four parts: the current execution statement s, the call statement call, the control dependent predicate slice slicePred, and the slice of the variables influence in the defined variable influence set Influence[def].Program slicing can extract a set of statements from a program according to a certain slicing criterion, we use the fault-related criteria of failed runs by program slicing to collect the slices. Our approach computes the slices according to the Program Dependence Graph (PDG) and the slice of statements with a forward computing algorithm. Dynamic slicing of programs can narrow the search for software fault localization by obtaining statements related to the slicing criterion. Then, we compute the suspiciousness of entities in the dynamic slice by the formulas of fault localization to generate a fault localization report. For passed tests and failed tests, more uncovered entities with passing executions and covered entities with failed executions should be assigned a higher degree of suspiciousness. Moreover, the covered entities by more passed executions and the uncovered entities by the failed executions should be assigned a lower suspiciousness. Therefore, the number of covered entities should have more weight than the number of uncovered entities in the suspiciousness calculation. Over the past few decades, dozens of doubtful formulas have been proposed and evaluated through empirical research. In our approach, we apply Tarantula , Naish1 i.e., Tarantula, Naish1, GP02, Ochiai, Jaccard, and GP13) and compare our approach and other approaches using the same evaluation formula, respectively. For example, Tarantula is applied to the original dynamic slice for locating faults, which is denoted as D-Tarantula; Tarantula is applied to the improved dynamic slice to locate faults, which is denoted as DS-Tarantula. Whereas, Tarantula is applied to ADBS to locate faults, which is denoted as ADBS-Tarantula.To verify the effectiveness of our approach, we compare our approach with both the original dynamic slice and ADBS on the fault localization. In this Section, 15 Java programs are selected as the benchmark for testing, and the detailed description is shown in Our approach applied to single-bug version programs to locate fault. We removed two versions with no test case execution failure and eight versions with multiple faults from the 41 fault versions of jtcas, and selected the remaining 31 versions for testing. We randomly selected 10 of the 23 incorrect versions of the totAs shown in fourth column of In order to evaluate the effectiveness of various fault localization approaches, we use the following four criteria to evaluate the effectiveness from different perspectives.Cumulative Number of Statements Examined: The number of cumulative examined statements for each program is the number of total statements that need to be checked when m faults are located in m fault versions. For m fault versions of any given program, M(i) and N(i) represent the number of statements that need to be checked when approach M and approach N locate a fault. If Cost Score: Cost criterion is to evaluate the effectiveness of fault localization from the perspective of relative index, which was first proposed by Because the faulty statement may share the same suspiciousness with others, the programmer might need to examine only one of these statements fortunately (best cases) or to examine all these statements to locate the real fault unfortunately (worst cases) . In the EXAM Score: To more intuitively compare the effectiveness of different approaches, we also use EXAM indicators for evaluation. The EXAM indicator is the ratio of the fault detection rate (% of faults located) to the code examined rate (% of code examined) (xamined) , as showIn the early research, Recall at Top-N: This dependent variable measures the number of faults with at least one faulty element within Top-N in the ranked list. This metric emphasizes earlier fault detection and has been widely used in fault localization work (ion work . KochharWe can observe the cumulative number of statements examined between our technique and the original dynamic slice in Furthermore, we can observe the cumulative number of statements examined between our technique and ADBS in %, 4.79%, 4.87%, 4.53%, 4.76% and 5.08%, respectively. Furthermore, the cost of DS-Tarantula is much smaller than that of D-Tarantula on schedule2, which is 6.27%, whereas the cost of DS-GP02 is much smaller than that of D-GP02 on schedule2, which is 8.52%.As shown in Than, we compare the average cost of fault localization our method and ADBS on each topic, as shown in Additionally, to more intuitively show the improvement of our approach over the compared techniques, we use the Exam metric to evaluate the effectiveness. As shown in As shown in In this section, we evaluate the time cost of the improved slice, the ADBS slice, and the original slice fault localization using the tarantula formula. As shown in In this subsection, we discuss the potential threats to the validity of our empirical study.Threats to external validity are about whether the observed experimental results can be generalized to other subjects. To guarantee the representativeness, we choose a large number of programs from the widely used Simens, Linux, and Defects4J suites. Our experiments consider both C and Java programs and include both seeded and real bugs. We realize that there is no perfect empirical study and there must be some tricky programs and bugs not considered in our experiments. We plan to enlarge our subjects in our future work.The internal validity of this work lies in the accuracy of the slicing result computed by our approach. To avoid faults in our tool implementation, we prepared our data carefully and tested our approach with simple programs.Threats to construct validity are about whether the performance metrics used in the empirical studies reflect the real-world situation. In the experiment, we used the cumulative number of statements examined, Cost, EXAM, and Top-N other indicators. We realize some indicators were not considered in our experiments. Therefore, using more widely metrics is our future work.alias analysis on sliced programs. The second type of method is the combination of program slicing and program spectrum methods for fault localization. For example, After Spectrum-based tuning is one of the most popular tuning techniques in the automated tuning process. The spectral information of program execution is used to calculate the suspiciousness of each statement , Ochiai In SBFL, ranking is based solely on the suspiciousness score of each element. Elements with high scores are placed at the beginning of the list, and elements with low scores are placed at the bottom. To enhance the impact of failed test cases on fault localization, In this article, we have proposed the improved dynamic slices to improve the effectiveness of fault localization. In details, we collect the dynamic slice information of program execution. Secondly, we use dynamic slices and execution results to build a hybrid spectrum. Last, a fault localization report is finally obtained by computing the suspiciousness of each statement in the hybrid slice spectrum. In the empirical study, our approach is compared with three fault localization approaches. Experimental results indicate that our approach outperforms other compared approaches. Furthermore, it can reduce approximately 1% to 17.27% of the average cost of examined code significantly. In the future, we will continue to verify the performance of our proposed technique on more subjects and further improve the accuracy of fault localization.In the future, we want to further consider the following issues. Firstly, we plan to apply our approach to locate multiple faults. Secondly, we want to use the methods like deep learning to deal with the massive information of slice computing, so that we can improve our method to locate faults in more large-scale and real-world applications. Last but not least, we want to apply our approach to more subjects written by other programming languages such as C++, Python and conduct more detailed empirical studies.10.7717/peerj-cs.1071/supp-1Supplemental Information 1Click here for additional data file.10.7717/peerj-cs.1071/supp-2Supplemental Information 2Click here for additional data file.10.7717/peerj-cs.1071/supp-3Supplemental Information 3Click here for additional data file.


Case Presentation. Seven-day-old female neonate was brought by her parents after they observe reddish discoloration of her genitalia. Upon examination, there was a vertical perineal wet groove-like unkeratinized erythematous lesion with no bleeding, sign of infection, or inflammation that extends from the posterior vaginal fourchette to the anal rim. The newborn was sent home after counseling with a reassessment plan in case of complications.  Perineal groove is a rare congenital anomaly characterized by nonepithelialized mucous membrane that appears as an erythematous sulcus in the perineal midline, extending from the posterior vaginal fourchette to the anterior anal orifice. It is one of the uncommon anomalies of anogenital region that is unknown to many clinicians, and it is usually misdiagnosed as an anal fissure, perineal trauma, diaper dermatitis, infection, or sexual abuse.  Recognition of the congenital perineal groove at birth is important for the health care providers to deliver an appropriate parental counseling and appropriate follow-up. Perineal groove is a rare congenital anomaly characterized by nonepithelialized mucous membrane that appears as an erythematous sulcus in the perineal midline, extending from the posterior vaginal fourchette to the anterior anal orifice , 2. It iA seven-day-old female newborn from Bahir Dar city was brought to Afilas General Hospital by her parents with the complaint of reddish discoloration of the skin around the genitalia (like fresh wound). The neonate was born to a 30-year-old Para II mother through spontaneous vaginal delivery with no instrumentation at gestational age of 39 + 6 weeks with birth weight of 3400 grams and Apgar score of 8 and 10 in the first and fifth minutes, respectively. The total duration of labor was 10 hours and rupture of membrane was intrapartum. The mother had regular antenatal care follow-up and it was uneventful. The mother has no history of cigarette smoking, alcohol consumption, or substance abuse. Both the father and mother have no history of congenital birth defects in the family. There was no history of local trauma, defecation issues, and urinary problem with the newborn.Physical examination of the neonate was normal in other body systems. On genital examination, there was around 1.5 cm wet groove-like erythematous vertical lesion which extends from the posterior vaginal fourchette at 6 o'clock to the anal rim . The lesPerineal groove is generally a wet sulcus lesion extending from the posterior vaginal fourchette to the anterior anus . It is mIt is a congenital malformation that consists of three main features: (1) a wet groove in the midperineum between the fourchette and the anus, (2) normal formation of the vestibule including the urethra and vagina, and (3) hypertrophy of the minoral tails that skirt the perineum and course posterior to join at the anus or surround it .There are several controversial embryological theories regarding the formation of perineal groove , 4. ThisPerineal groove could be complete or incomplete. The complete perineal groove extends from the vaginal fourchette to the anus, while the incomplete type either extends from the vaginal fourchette to the middle of the perineum or from the anus to the middle of the perineum .Few cases have been published in medical literatures , 10. DefPerineal groove usually resolves spontaneously, and complete epithelialization occurs by the age of 1 to 2 years . SurgicaCorrect recognition is important to avoid misdiagnosis and unnecessary investigations. I report this case to raise awareness and for doctors to provide appropriate advice and follow-up recommendations for parents of affected infants.