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Hyperspectral band selection, which aims to select a small number of bands to reduce data redundancy and noisy bands, has attracted widespread attention in recent years. Many effective clustering-based band selection methods have been proposed to accomplish the band selection task and have achieved satisfying performance. However, most of the previous methods reshape the original hyperspectral images (HSIs) into a set of stretched band vectors, which ignore the spatial information of HSIs and the difference between diverse regions. To address these issues, a graph regularized spatial–spectral subspace clustering method for hyperspectral band selection is proposed in this paper, referred to as GRSC. Specifically, the proposed method adopts superpixel segmentation to preserve the spatial information of HSIs by segmenting their first principal component into diverse homogeneous regions. Then the discriminative latent features are generated from each segmented region to represent the whole band, which can mitigate the effect of noise on the band selection. Finally, a self-representation subspace clustering model and an l 2 , 1 -norm regularization are utilized to explore the spectral correlation among all bands. In addition, a similarity graph between region-aware latent features is adaptively learned to preserve the spatial structure of HSIs in the latent representation space. Extensive classification experimental results on three public datasets verify the effectiveness of GRSC over several state-of-the-art methods. The demo code of this work is publicly available at https://github.com/WangJun2023/GRSC. [ABSTRACT FROM AUTHOR]

• Graph attention network (GAT) is a promising framework for message passing on graphs. • Increasing the attention range to more than one-hop neighbors can negatively affect the performance of GAT, reflecting the over-smoothing risk of graph neural networks in general. • The key idea is to contextualize each node with its "structural fingerprint" that can automatically adjust to the local graph topology and edge connections in the neighborhood of the node. • Our approach provides a useful platform for different subspace of node features and various spatial scale of graph structures to "cross-talk" with each other through multi-head attention, which is more flexible than existing attention mechanism using a fixed, minimal spatial attention scale. Graph attention network (GAT) is a promising framework for message passing on graphs, but how to exploit rich, high-order structural information in the attention mechanism is still an open challenge. Furthermore, increasing the attention range to more than one-hop neighbors can negatively affect the performance of GAT, reflecting the over-smoothing risk of graph neural networks in general. In this paper, we propose an "adaptive structural fingerprint" model to fully exploit complex graph topology in graph attention networks. The key idea is to contextualize each node with its "structural fingerprint" that can automatically adjust to the local graph topology and edge connections in the neighborhood of the node. By doing this, structural interactions between the nodes can be evaluated more accurately and better confined to relevant neighbors, thus contributing to an improved attention mechanism and clearer cluster boundary. Furthermore, our approach provides a useful platform for different subspace of node features and various spatial scale of graph structures to "cross-talk" with each other through multi-head attention, which is more flexible than existing attention mechanism using a fixed, minimal spatial attention scale. Encouraging results are observed on a number of benchmark data sets including citation and social networks. [ABSTRACT FROM AUTHOR]

Purpose: This finite element analysis assessed lateral compression (LC-1) fracture stability using machine learning for morphological mapping and classification of pelvic ring stability. Methods: Computed tomography (CT) files of LC-1 pelvic fractures were collected. After morphological mapping and producing matrix data, we used K-means clustering in unsupervised machine learning to classify the fractures. Based on these subtypes, we manually added fracture lines in ANSYS software. Finally, we performed a finite element analysis of a normal pelvis and eight fracture subtypes based on von Mises stress and total deformation changes. Results: A total of 218 consecutive cases were analyzed. According to the three main factors—zone of sacral injury and completion, pubic ramus injury side, and the sagittal rotation of the injured hemipelvis—the LC-1 injuries were classified into eight subtypes (I–VIII). No significant differences in stress or deformation were observed between unilateral and bilateral public ramus fractures. Subtypes VI and VIII showed the maximum stress while subtypes V–VIII showed the maximum deformation in the total pelvis and sacrum. The subtypes did not differ in superior public ramus deformation. Conclusions: Complete fracture of sacrum zones 2/3 may be a feature of unstable LC-1 fractures. Surgeons should give surgical strategies for subtypes V–VIII. [ABSTRACT FROM AUTHOR]

It is a significant challenge to classify images using limited training samples. To this end, we formulate image classification as a multi-task multi-view (MTMV) learning problem and propose a novel Pareto optimization-based method to find the solution. Specifically, we first build a multi-objective multiple-relations-constrained MTMV model called M4 to formulate the procedure of image classification. This model integrates comprehensive relations so that more knowledge can be used when classifying the images. We formulate the model as a multi-objective optimization problem, which addresses conflicts between the inconsistencies of each item in the model and the limitation on the number of relations. To generate the final classifier for each image classification task, an effective Pareto optimization-based algorithm Pareto-M4 is proposed. Pareto-M4 first generates the Pareto-optimal solutions using a novel multi-objective solver MOQPSO and then obtains the final classifiers from all the Pareto-optimal solutions using a recombination procedure. Experiments on various real-world image data sets demonstrate the effectiveness of the proposed image classification method when limited training samples are given. [ABSTRACT FROM AUTHOR]

Neuroimaging techniques can measure structural and functional brain connectivity with unprecedented detail in vivo. This so-called brain connectome can be represented as high dimensional matrices corresponding to edge weights in graphs. After measuring the matrices of two cohorts (i.e., patients and healthy controls), one is often required to formulate computational network models for effective feature engineering to draw discriminative distinctions between the cohorts, as well as estimate the associated statistical significance. We designed a novel method to reveal the intrinsic features of functional matrices of discriminative power for group comparison. More specifically, by encouraging co-selection of edges connected to the same node, we preserved the discriminative edges to maximum extent. To reduce the false positive rate of the extracted discriminative edges, an optimization procedure was developed to evaluate the significance of these edges and remove trivial ones. We validated the proposed method using both synthetic data and real benchmarks, and compared it to \ell1 regularized logistic regression, univariate t-test and stability selection. The experimental results clearly showed that the proposed approach outperformed the three competing methods under various settings. In addition to increasing the F-measure of feature selection, our approach captured the endogenous, discriminative connectivity patterns consistent with recent findings in biomedical literature. This data-driven method paves a new avenue of enquiry into the inherent nature of network models for functional brain connectomes. [ABSTRACT FROM AUTHOR]

An electronic nose (E-nose) and an electronic tongue (E-tongue) have been used to characterise strawberry juices based on different processes (i.e. Microwave Pasteurisation, Steam Blanching, High Temperature Short Time Pasteurisation, Frozen-Thawed, and Freshly Squeezed). Quality parameters (vitamin C, pH, total soluble solid, total acid and sugar/acid ratio) were detected by traditional measuring methods. For qualitative discrimination, E-tongue system reached higher accuracy than E-nose did, and lower than the simultaneous utilisation. For quantitative prediction, E-tongue system had better performance ( R 2 ranged from 0.7294 to 0.8622 for the calibration, and R 2 ranged from 0.7362 to 0.9527 for the validation) than E-nose did ( R 2 ranged from 0.5344 to 0.8741 for the calibration, and R 2 ranged from 0.4444 to 0.7258 for the validation), and worse than the simultaneous utilisation ( R 2 ranged from 0.9627 to 0.9834 for the calibration, and R 2 ranged from 0.8553 to 0.8959 for the validation). Either E-nose or E-tongue was suited to classify strawberry juice, but was insufficient to predict the values of quality parameters (vitamin C, pH, total soluble solid, total acid, and sugar/acid ratio). This work indicates that the simultaneous utilisation of E-nose and E-tongue can discriminate processed juices and predict juice quality parameters successfully for the food industry. [ABSTRACT FROM AUTHOR]

A potentiometric electronic tongue (PE-tongue) and a voltammetric electronic tongue (VE-tongue) were used as rapid techniques to classify and predict the honey samples from different floral and geographical origins. The PE-tongue, which was named α-ASTREE, was developed by Alpha M.O.S. (Toulouse, France), and it comprises seven potentiometric chemical sensors. The VE-tongue was self-developed at Zhejiang University and comprises six metallic working sensors. Four types of honey of different floral origins (acacia, buckwheat, data, and motherwort) and four types of acacia honey of different geographical origins were classified by both multisensor systems. Multivariate statistical data analysis techniques such as principal component analysis (PCA) and discriminant function analysis (DFA) were used to classify the honey samples. Both types of electronic tongue have good potential to classify the honey samples, and the positions of the data point for the samples in the PCA score plots based on the VE-tongue were much more closely grouped. Three regression modes, principal component regression (PCR), partial least squares regression (PLSR), and least squared-support vector machines (LS-SVM), were applied for category forecasting. These regression models exhibited a clear indication of the prediction ability of the two types of electronic tongue, and a positive trend in the prediction of the floral and geographical origin of honey was found. Moreover, the performance of these regression models for predicting the four types of honey of different geographical origins by the VE-tongue is very stable. [ABSTRACT FROM AUTHOR]

Abstract: Various clustering algorithms have been developed since conventional hierarchical cluster analysis (HCA) and partitioning clustering algorithms have their own limitations and scopes of applications. However, in the area of e-nose where clustering is applied, the conventional algorithms (mostly HCA) still play a dominant role. In addition, comparison among different clustering methods or validation of clustering results was seldom mentioned. In this paper, we present a state-of-the-art clustering method – spectral clustering – and compare it with six conventional clustering methods: K-clustering (ISODATA, FCM and k-means) and HCA (single linkage, complete linkage and Ward's). Three external validation criteria – mutual information criteria (MI), precision and rand index (RI) – were used to evaluate clustering performances on three independent e-nose datasets. The spectral clustering outperforms with statistical significance (alpha=0.05) the performance of other methods, and the single linkage presents the worst (unacceptable) clustering result. In addition, the proposed approach – cluster validation criteria in combination with majority voting – in a way makes clustering a semi-supervised classification technique. Using this approach it is possible to compare clustering based semi-supervised methods with classification methods to find which method is better for discrimination of a certain e-nose dataset. [Copyright &y& Elsevier]

Magnetic response of a superconductor depends on the thermodynamic stability of a vortex in the material. Here we show that the vortex stability has a close relation with the ratio of the magnetic field at the vortex core center to the thermodynamic critical field. This finding provides a new criterion for the classification of the superconductors according to their magnetic responses. [ABSTRACT FROM AUTHOR]

Most traditional hyperspectral image (HSI) classification methods relied on hand-crafted or shallow-based descriptors, which limits their applicability and performance. Recently, deep learning has gradually become the mainstream method of HSI classification, because it can automatically extract deep abstract features for classification. However, it remains a challenge to learn more meaningful features for HSI classification from a small training sample set. In this paper, a 3D cascaded spectral–spatial element attention network (3D-CSSEAN) is proposed to solve this issue. The 3D-CSSEAN integrates the spectral–spatial feature extraction and attention area extraction for HSI classification. Two element attention modules in the 3D-CSSEAN enable the deep network to focus on primary spectral features and meaningful spatial features. All attention modules are implemented though several simple activation operations and elementwise multiplication operations. In this way, the training parameters of the network are not added too much, which also makes the network structure suitable for small sample learning. The adopted module cascading pattern not only reduces the computational burden in the deep network but can also be easily operated via plug–expand–play. Experimental results on three public data sets show that the proposed 3D-CSSEAN achieved comparable performance with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Nonlinear spiking neural P (NSNP) systems are neural-like membrane computing models with nonlinear spiking mechanisms. Because of this nonlinear spiking mechanism, NSNP systems can show rich nonlinear dynamics. Reservoir computing (RC) is a novel recurrent neural network (RNN) and can overcome some shortcomings of traditional RNNs. Based on NSNP systems, we developed two RC variants for time series classification, RC-SNP and RC-RMS-SNP, which are without and integrated with reservoir model space (RMS), respectively. The two RC variants use NSNP systems as the reservoirs and can be easily implemented in the RC framework. The proposed two RC variants were evaluated on 17 benchmark time series classification datasets and compared with 16 state-of-the-art or baseline classification models. The comparison results demonstrate the effectiveness of the proposed two RC variants for time series classification tasks. [ABSTRACT FROM AUTHOR]

Using the zone fire model CFAST as the simulation engine, time series data for building sensors, such as heat detectors, smoke detectors, and other targets at any arbitrary locations in multi-room compartments with different geometric configurations, can be obtained. An automated process for creating inputs files and summarizing model results, CData, is being developed as a companion to CFAST. An example case is presented to demonstrate the use of CData where synthetic data is generated for a wide range of fire scenarios. Three machine learning algorithms: support vector machine (SVM), decision tree (DT), and random forest (RF), are used to develop classification models that can predict the location of a fire based on temperature data within a compartment. Results show that DT and RF have excellent performance on the prediction of fire location and achieve model accuracy in between 93% and 96%. For SVM, model performance is sensitive to the size of training data. Additional study shows that results obtained from DT and RT can be used to examine the importance of each input feature. This paper contributes a learning-by-synthesis approach to facilitate the utilization of a machine learning paradigm to enhance situational awareness for fire fighting in buildings. [ABSTRACT FROM AUTHOR]

Recently, convolutional neural networks (CNNs) have been widely used for synthetic aperture radar (SAR) image classification because of their powerful feature extraction ability and high performance. However, extracting discriminative features with limited training samples is still a challenge. Moreover, some samples may be image edge samples, which often contain multiple image categories, thus deteriorate classification accuracy. To address these issues, we propose a novel classification framework, named dense-connected global covariance network (DGCNet) with edge sample constraint (ESC). First, a dense-connected sub-network was designed, which can connect different convolutional layers of conventional CNN to strengthen feature propagation, encourage feature reuse, and alleviate gradient vanishing problem. Then, a global covariance pooling layer was introduced to fully exploit the second-order information of deep features and reduce the number of training parameters. Finally, an ESC strategy was integrated into DGCNet to further improve the classification performance by assigning a smaller weight to edge samples than non-edge samples during the training process. Experimental results on two datasets demonstrated that the proposed method achieves better classification results than several popular classification methods with limited training samples. [ABSTRACT FROM AUTHOR]

Background: Long noncoding RNAs (lncRNAs) are widely involved in the initiation and development of cancer. Although some computational methods have been proposed to identify cancer-related lncRNAs, there is still a demanding to improve the prediction accuracy and efficiency. In addition, the quick-update data of cancer, as well as the discovery of new mechanism, also underlay the possibility of improvement of cancer-related lncRNA prediction algorithm. In this study, we introduced CRlncRC, a novel Cancer-Related lncRNA Classifier by integrating manifold features with five machine-learning techniques. Results: CRlncRC was built on the integration of genomic, expression, epigenetic and network, totally in four categories of features. Five learning techniques were exploited to develop the effective classification model including Random Forest (RF), Naïve bayes (NB), Support Vector Machine (SVM), Logistic Regression (LR) and K-Nearest Neighbors (KNN). Using ten-fold cross-validation, we showed that RF is the best model for classifying cancer-related lncRNAs (AUC = 0.82). The feature importance analysis indicated that epigenetic and network features play key roles in the classification. In addition, compared with other existing classifiers, CRlncRC exhibited a better performance both in sensitivity and specificity. We further applied CRlncRC to lncRNAs from the TANRIC (The Atlas of non-coding RNA in Cancer) dataset, and identified 121 cancer-related lncRNA candidates. These potential cancer-related lncRNAs showed a certain kind of cancer-related indications, and many of them could find convincing literature supports. Conclusions: Our results indicate that CRlncRC is a powerful method for identifying cancer-related lncRNAs. Machine-learning-based integration of multiple features, especially epigenetic and network features, had a great contribution to the cancer-related lncRNA prediction. RF outperforms other learning techniques on measurement of model sensitivity and specificity. In addition, using CRlncRC method, we predicted a set of cancer-related lncRNAs, all of which displayed a strong relevance to cancer as a valuable conception for the further cancer-related lncRNA function studies. [ABSTRACT FROM AUTHOR]

In this paper we propose a k -nearest neighbors (kNN) classifier optimized by P systems, called kNN-P, which can improve the performance of the original kNN classifier. A P system consisting of multiple cells is considered as its computational framework. Under the control of both evolution rules and communication rules, each cell determines the optimal set of k -nearest neighbors for a test sample. The proposed algorithm is evaluated on eighteen benchmark datasets and compared with classical kNN algorithm and eight recently developed improved algorithms. Experimental results demonstrate the availability and effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Gated spiking neural P (GSNP) model is a recently developed recurrent-like network, which is abstracted by nonlinear spiking mechanism of nonlinear spiking neural P systems. In this study, a modification of GSNP is combined with attention mechanism to develop a novel model for sentiment classification, called attention-enabled GSNP model or termed as AGSNP model. The AGSNP model has two channels that process content words and aspect item respectively, where two modified GSNPs are used to obtain dependencies between content words and between aspect words. Moreover, two attention components are used to establish semantic correlation between content words and aspect item. Comparative experiments on three real data sets and several baseline models are conducted to verify the effectiveness of the AGSNP model. The comparison results demonstrate that the AGSNP model is competent for aspect-level sentiment classification tasks. • We propose a modification of gated spiking neural P systems. • We propose a new model for sentiment classification, termed as AGSNP model. • We design a sentiment classification framework based on AGSNP. [ABSTRACT FROM AUTHOR]

A novel transfer support vector machine called TSVM-GP with group probabilities is proposed for the scenarios where plenty of labeled data in the source domain and the group probabilities of unlabeled data in the target domain are available. TSVM-GP integrates a transfer term and group probabilities into the support vector machine (SVM) to improve the classification accuracy. In order to reduce the high computational complexity of TSVM-GP, the scalable version of TSVM-GP called scalable transfer support vector machine with group probabilities (STSVM-GP) is further developed by selecting the representative set of the training samples as the training data in the source domain. Experimental results on synthetic datasets as well as several real-world datasets show the effectiveness of the proposed classifiers, and especially STSVM-GP is very feasible for large scale transfer datasets. [ABSTRACT FROM AUTHOR]

Two key problems for crowd-sourcing systems are motivating contributions from participants and ensuring the quality of these contributions. Games have been suggested as a motivational approach to encourage contribution, but attracting participation through game play rather than intrinsic interest raises concerns about the quality of the contributions provided. These concerns are particularly important in the context of citizen science projects, when the contributions are data to be used for scientific research. To assess the validity of concerns about the effects of gaming on data quality, we compare the quality of data obtained from two citizen science games, one a “gamified” version of a species classification task and one a fantasy game that used the classification task only as a way to advance in the game play. Surprisingly, though we did observe cheating in the fantasy game, data quality (i.e., classification accuracy) from participants in the two games was not significantly different. As well, data from short-time contributors was also at a usable level of accuracy. Finally, learning did not seem to affect data quality in our context. These findings suggest that various approaches to gamification can be useful for motivating contributions to citizen science projects. [ABSTRACT FROM AUTHOR]

Objective: To classify the fabellae and discuss the relationship between the classification of fabellae and the severity of knee osteoarthritis (KOA) in Chinese. Methods: From February 2019 to February 2020, 136 patients were measured and classified using three‐dimensional computed tomography (CT) reconstruction. According to the CT imaging characteristics, the fabellae were divided into five types: type I, a fabella on the lateral femoral condyle; type II, a fabella on the medial femoral condyle; type III, a fabella on the lateral femoral condyle and a fabella on the medial femoral condyle; type IV, two fabellae on the medial femoral condyle; and type V, two fabellae on the lateral femoral condyle. The severity of KOA was assessed on the Recht grade by magnetic resonance imaging (MRI). The data were analyzed with SPSS 24.0. Results: The classification of fabellae were correlated with KOA grades (χ2 = 35.026, P < 0.05). In terms of KOA grades, grade I and grade II were occupied most by fabellar type II (32, 72.8%); type II and other types showed significant statistical difference (P < 0.05). Grade I and grade II were also mainly fabellar type IV (four, 100%). Fabellar type V's biggest component was grade III and grade IV (six, 75%). Type IV and type V showed significant statistical difference (P < 0.05). Conclusion: The classification of fabellae were correlated with KOA grades. The type II may mean the lower KOA grades while type V may mean the higher KOA grades. [ABSTRACT FROM AUTHOR]

An electronic nose (E-nose) has been used to characterize five types of strawberry juices based on different processing approaches (i.e., Microwave Pasteurization, Steam Blanching, High Temperature Short Time Pasteurization, Frozen–Thawed, and Freshly Squeezed). Juice quality parameters (vitamin C and total acid) were detected by traditional measuring methods. Multivariate statistical methods (Principle Component Analysis, Linear Discriminant Analysis, Multiple Linear Regression, and Partial Least Squares Regression) and neural networks (Extreme Learning Machine (ELM), Learning Vector Quantization and Library Support Vector Machines) were employed for qualitative classification and quantitative regression. ELM showed best performances on classification and regression, indicating that ELM would be a good choice for E-nose data treatment. Results provide promising principles for the elaboration of E-nose which could be used to discriminate processed juices and to predict juice quality parameters based on appropriate algorithms for the beverage industry. [ABSTRACT FROM AUTHOR]

Reservoir computing (RC) is a novel class of recurrent neural networks (RNN) models. Nonlinear spiking neural P (NSNP) systems are neural-like computing models with nonlinear spiking mechanisms. By introducing NSNP systems as the reservoir, we propose a new RC model for time series classification task, termed TSC-NSNP model. However, due to the high-dimensional nature of the reservoir state space, the TSC-NSNP model, like existing RC models, will encounter some challenges. To address the challenges. we utilize the reservoir model space representation and dimensionality reduction method to propose two improved models, termed TSC-DR-NSNP model and TSC-RMS-NSNP model. The three RC models can be easily realized and learnt in the RC framework. The proposed three RC models are evaluated on 21 benchmark time series classification data sets, and are compared with 20 classification models. The comparisons demonstrate the effectiveness of the presented three RC models for time series classification tasks. [ABSTRACT FROM AUTHOR]

Although long noncoding RNAs (lncRNAs) have significant tissue specificity, their expression and variability in single cells remain unclear. Here, we developed ColorCells (http://rna.sysu.edu.cn/colorcells/), a resource for comparative analysis of lncRNAs expression, classification and functions in single-cell RNA-Seq data. ColorCells was applied to 167 913 publicly available scRNA-Seq datasets from six species, and identified a batch of cell-specific lncRNAs. These lncRNAs show surprising levels of expression variability between different cell clusters, and has the comparable cell classification ability as known marker genes. Cell-specific lncRNAs have been identified and further validated by in vitro experiments. We found that lncRNAs are typically co-expressed with the mRNAs in the same cell cluster, which can be used to uncover lncRNAs' functions. Our study emphasizes the need to uncover lncRNAs in all cell types and shows the power of lncRNAs as novel marker genes at single cell resolution. [ABSTRACT FROM AUTHOR]

Abstract: Random forest (RF) has been proposed on the basis of classification and regression trees (CART) with “ensemble learning” strategy by Breiman in 2001. In this paper, RF is introduced and investigated for electronic tongue (E-tongue) data processing. The experiments were designed for type and brand recognition of orange beverage and Chinese vinegar by an E-tongue with seven potentiometric sensors and an Ag/AgCl reference electrode. Principal component analysis (PCA) was used to visualize the distribution of total samples of each data set. Back propagation neural network (BPNN) and support vector machine (SVM), as comparative methods, were also employed to deal with four data sets. Five-fold cross-validation (CV) with twenty replications was applied during modeling and an external testing set was employed to validate the prediction performance of models. The average correct rates (CR) on CV sets of the four data sets performed by BPNN, SVM and RF were 86.68%, 66.45% and 99.07%, respectively. RF has been proved to outperform BPNN and SVM, and has some advantages in such cases, because it can deal with classification problems of unbalanced, multiclass and small sample data without data preprocessing procedures. These results suggest that RF may be a promising pattern recognition method for E-tongues. [Copyright &y& Elsevier]

Aspect-level sentiment classification still remains a challenge: how to capture contextual semantic correlation between aspect word and content words more effectively. LSTM-SNP is a variant of long short-term memory (LSTM), inspired from nonlinear spiking mechanisms in nonlinear spiking neural P systems. To address the challenge, we develop a modification of LSTM-SNP and design a bidirectional LSTM-SNP based on the modification, termed as BiLSTM-SNP. Based on BiLSTM-SNP and attention mechanism, we propose a novel method for aspect-level sentiment classification. BiLSTM-SNP is used to capture semantic correlation between aspect word and content words, while attention mechanism is utilized to generate appropriate attention weights for hidden states of BiLSTM-SNP. Experiments on English and Chinese data sets are conducted on the proposed model and several baseline models. Experiment results on English and Chinese data sets demonstrate the effectiveness of the proposed model for aspect-level sentiment classification. [ABSTRACT FROM AUTHOR]

Sesame oil (SO) is a high-quality oil that is more expensive than other edible oils, and therefore becomes a target of economically motivated adulteration. An approach based on excitation-emission matrix (EEM) fluorescence and chemometric methods was applied for the rapid classification and determination of the authenticity of SO. First, a five-factor alternating trilinear decomposition (ATLD) model roughly completed the characterization of the fluorescent components in the edible oil samples, providing meaningful chemical information. Then, four chemometric methods, including linear discriminant analysis (LDA), partial least squares–discriminant analysis (PLS-DA), support vector machine (SVM) and unfolded partial least-squares discriminant analysis (UPLS-DA), were used to establish the models for the classification of SO and other edible oils (Model 1), and determine the authenticity of SO and adulterated SOs (Model 2). All models achieved good classification results. The combination of the second-order calibration algorithm (ATLD) and the pattern recognition algorithm (LDA, PLS-DA, or SVM) not only achieved the characterization of the components in edible oils but also realized the rapid detection of adulterated SOs. The proposed method is rapid, accurate, requires a simple sample pre-treatment and can be used to determine the authenticity and adulteration of high-quality edible oils. • A simple and rapid EEMs fluorescence was developed for adulteration of sesame oil for the first time. • ATLD decomposed the meaningful chemical composition of edible oils. • Discriminant model for detection was built by ATLD-LDA, ATLD-PLS-DA, ATLD-SVM and UPLS-DA. • All methods successfully identified various pure edible oils and adulterated sesame oils. [ABSTRACT FROM AUTHOR]

The ecological environment is gravely threatened by the buildup of microplastics (MPs) in soil. Currently, there are no established techniques for detecting MPs in soil. Some of the standard chemical detection methods now in use are time-consuming and cumbersome. This research suggested a method for identifying soil microplastic polymers (MPPs) based on convolutional neural networks (CNN) and hyperspectral imaging (HSI) technologies to address this issue. The categorization model for MPPs on the soil surface was first established by simulating the natural soil environment in the lab. While decision tree (DT) and support vector machine (SVM) models' classification accuracy was 87.9 % and 85.6 %, respectively, that of CNN was 92.6 %. The HIS and CNN model combination produced the best classification results out of all of these models. Secondly, farmland in Guangzhou's Tianhe, Panyu, and Zengcheng districts was sampled for surface soil samples measuring 0–20 cm in order to confirm the model's accuracy in the actual environment. Before data analysis, the physicochemical properties of soil samples were determined by a standardization scheme. MPs in soil samples were extracted by traditional chemical detection method and their chemical properties were obtained as the results of the control group. Then, CNN was applied to hyperspectral data from soil samples collected for MPs detection. Finally, it was demonstrated that the physical and chemical properties of the soil have an impact on the accuracy of the model through the investigation of the physical and chemical characteristics of soil samples from three distinct areas. On the other hand, the results indicated that the suggested technique offers quick and non-destructive results for MPPs detection when comparing the detection results of hyperspectral and conventional chemical methods. [Display omitted] • This research suggests a new method for identifying soil microplastic polymers (MPPs). • The HIS and CNN model combination produces the best classification results out of all of these models. • CNN is applied to hyperspectral data from soil samples collected for MPs detection. • The technique offers quick and non-destructive results for MPPs detection for soil microplastics. [ABSTRACT FROM AUTHOR]

Background Two editions of the International Classification of Headache Disorders (ICHD) diagnostic criteria for 'Headache attributed to an intracranial endovascular procedure' have been published, in 2004 and 2013. Despite studies that have suggested that the former is not very practical, the ICHD-3 beta did not contain major changes. Moreover, so far no consensus exists regarding characteristics of headache after intracranial endovascular procedure. Thus, there is a need for sound suggestions to improve the ICHD-3 beta diagnostic criteria. Methods Using a prospective design, we identified consecutive patients with unruptured intracranial aneurysms (UIAs) with neuroendovascular treatment from January 2014 to December 2014. Results In total, 73 patients were enrolled, and 58 patients ultimately completed the 6-month follow-up. After the procedure, five of the 29 patients (17.2%) with pre-existing headache experienced marked worsening after the procedure, while seven of the 29 patients without prior headache developed new-onset headache post-procedurally. The headaches started within 24 hours, with a mean duration of 24-72 hours. The headaches were moderate to severe. The eligibility of these events to be considered headaches caused by neuroendovascular procedures according to the ICHD-3 beta diagnostic criteria for designation was far from ideal. Conclusions Most cases of markedly worsening headaches and new-onset headaches started within 24 hours and persisted longer than that specified in the ICHD-3 beta diagnostic criteria. Moreover, considering that some items are not very practical, the ICHD-3 beta diagnostic criteria should be revised in the light of recent literature reports. [ABSTRACT FROM AUTHOR]

As substantial parts of process monitoring, fault classification techniques have been widely utilized in modern industries. However, most methods can only perform well under specific condition, which indicates that it is always difficult to ensure the classification efficiency for complex industrial processes using only one method. In this paper a weighted decision fusion system is proposed where an extended analytic hierarchy process (EAHP) structure is designed to give a comprehensive explanation for the weights assigned to the original fault classification results. Firstly, the fault category information is embedded in the EAHP structure, which makes it possible to consider both fault-wise and classifier-wise information in the fault classification. Secondly, an overall priority (OP) matrix is proposed to provide full prior knowledge for all classifiers. Different from previous researches, traditional OP vectors are replaced by the new-designed OP matrix which can contain more information about fault category. Thirdly, another confidence matrix is carried out to update the classification results. Compared with previous state-of-art, the confidence matrix can be adjusted according to the specific original classification result Finally, the effectiveness of the proposed method is verified by a numerical example and the Tennessee Eastman process (TEP) where the proposed decision fusion system shows superior fault classification results in both experiments. [ABSTRACT FROM AUTHOR]