Domain adaptive retrieval aims to transfer knowledge from a labeled source domain to an unlabeled target domain, enabling effective retrieval while mitigating domain discrepancies. However, existing methods encounter several fundamental limitations: 1) neglecting class-level semantic alignment and excessively pursuing pair-wise sample alignment; 2) lacking either pseudo-label reliability consideration or geometric guidance for assessing label correctness; 3) directly quantizing original features affected by domain shift, undermining the quality of learned hash codes. In view of these limitations, we propose Prototype-based Semantic Consistency Alignment (PSCA), a two-stage framework for effective domain adaptive retrieval. In the first stage, a set of orthogonal prototypes directly establishes class-level semantic connections, maximizing inter-class separability while gathering intra-class samples. During the prototype learning, geometric proximity provides a reliability indicator for semantic consistency alignment through adaptive weighting of pseudo-label confidences. The resulting membership matrix and prototypes facilitate feature reconstruction, ensuring quantization on reconstructed rather than original features, thereby improving subsequent hash coding quality and seamlessly connecting both stages. In the second stage, domain-specific quantization functions process the reconstructed features under mutual approximation constraints, generating unified binary hash codes across domains. Extensive experiments validate PSCA's superior performance across multiple datasets.

Artificial intelligence (AI) is a strategic technology leading the Fourth Industrial Revolution and has been widely applied across various industries. Based on data from Chinese A-share listed manufacturing firms from 2011 to 2023, this paper investigates the relationship between the intelligent development of manufacturing enterprises (IDME) and supply chain resilience (SCR). The study finds that the IDME can significantly enhance its SCR, particularly for large-scale enterprises, firms in strategic emerging industries (SEI), and enterprises located in regions with weak digital infrastructure and low levels of financial development. Mechanism analysis reveals that intelligent development has a positive effect on SCR by strengthening both internal capabilities and external linkages of enterprises. Specifically, it improves labor quality, enhances competitive capability, and optimizes internal governance; moreover, it promotes inter-firm collaborative innovation, reinforces information transparency, and strengthens supply–demand relationships. The conclusions of this study enrich the literature on the impact of intelligent development on SCR in manufacturing enterprises and provide empirical evidence and policy implications for promoting intelligent transformation and improving SCR in the manufacturing sector.

In Partial Multi-Label Learning (PML), each instance is associated with a candidate label set containing multiple relevant labels along with other false positive labels. Currently, most PML methods directly extract instance correlation from instance features while ignoring the candidate labels, which may contain more discriminative instance-related information. This paper argues that, with a well-designed model, more accurate instance correlation can be mined from the candidate labels to facilitate label disambiguation. To this end, we propose a novel PML method based on pseudo-label reconstruction (PML-PLR). Specifically, we first propose a novel orthogonal candidate label reconstruction method, which jointly optimizes with instance features to extract more consistent instance correlation. Then, we use instance correlation as reconstruction coefficient to reconstruct pseudo-labels. Subsequently, through local manifold learning, the reconstructed pseudo-labels are leveraged to propagate the consistency relationship between labels and instances, thereby improving the accuracy of pseudo-labels. Extensive experiments and analyses demonstrate that the proposed PML-PLR outperforms state-of-the-art methods.

With continuous development of the Internet of Vehicles (IoV), intelligence is gradually becoming the mainstream trend of future transportation. However, the increasing number of vehicles and their high mobility also bring greater challenges to privacy and security. Most of the current authentication schemes in IoV focus on a single domain, ignoring the cross-domain requirements brought by high mobility. To address these issues better, we propose an efficient anonymous cross-domain authentication scheme EACAS based on elliptic curve cryptography within the certificate-less cryptography. We incorporate the secret sharing with digital signature to provide the trustworthy authentication for cross-domain vehicles, and the batch verification improves the efficiency of authentication. Besides, we also propose a hierarchical pseudonym method for multidomain scenarios, allowing vehicles to update pseudonyms at arbitrary locations and can be revealed. The formal security proof and analysis demonstrate the security characteristics, and the comprehensive performance evaluation shows the efficiency.

The graph-based multiview clustering has gained significant attention due to its effectiveness in representing complex relationships among multiview data for enhanced clustering. Among the previous graph-based methods, the multiview graph learning (or graph fusion) technique has rapidly emerged as a promising direction, which, however, still suffers from two critical limitations. First, most of previous methods adopt a single-level of graph fusion, which lack the ability to go from single-level graph fusion to multilevel (deep) graph fusion. Second, they generally focus on constructing an optimal unified graph but cannot fully investigate the correlations among multiple views. Therefore, it is difficult to establish a comprehensive and obvious graph structure. In light of this, this article presents a new multiview graph learning method called deep similarity graph fusion (DSGF) for the multiview clustering task, where three pathways are simultaneously leveraged to fuse multilevel similarity into a unified graph. Particularly, multilevel graph fusion is utilized to obtain a view-specific similarity graph for each view and then fuse these single-view graphs (via three levels of graph fusion) into a robust graph, which takes advantage of deeper consensus information between various similarity graphs and improves the quality of the learned graph for the final spectral clustering process. Extensive experiments are conducted on six real-world multiview datasets, which demonstrate the highly competitive clustering performance of DSGF in comparison with state-of-the-art methods.

Conventional hash-based retrieval method rely on the assumption that the query and database are of the identical domain. However, cross-domain problem often occurs in real-world applications, leading to the unsatisfactory performance of existing hashing methods. Recently, some researchers have put forward domain adaptation retrieval (DAR) under the perspective of domain adaptation (DA) and achieved promising results. But the following limitations still exist: 1) a single function is used to handle two challenges, i.e., domain adaptation and hashing, which is not flexible to explore enough underlying information for simultaneously accomplishing these challenges well; 2) non-dominant features in the sample are ignored; 3) the dissimilarity structure of dissimilar samples is not taken into account. To address the above problems, we propose a novel framework named two-step strategy (TSS) for domain adaptation retrieval, which advocates dividing DAR into two steps: DA step and hashing step. A DA function and a hash function are learned to handle the above two challenges, respectively, making the process more reasonable. Additionally, a discriminant semantic fusion loss is proposed to improve the discriminative ability among classes. Unlike other works that focus on discovering dominant features, we exploit the neglected non-dominant features and assign them attention with sinusoidal semantic embedding, actively creating a clear separation between classes. At last, we present an adaptive similarity preserving loss to preserve the similarity structure of the original data in all intra-domain and inter-domain hash codes. Extensive experiments on various datasets demonstrate that the proposed TSS achieves state-of-the-art performance.

Hashing-based techniques present appealing solutions for cross-modal retrieval due to its low storage requirements and excellent query efficiency. The majority of cross-modal hashing methods typically adopt equal-length encoding scheme to represent multimodal data and achieve cross-modal similarity search. However, such scheme can be regarded as a relatively strict limitation, because it sacrifices the flexible representation of multimodal data in reality and cannot always guarantee the optimal retrieval performance. To address the challenge, this paper focuses on encoding heterogeneous data with varying hash lengths. To achieve this purpose, we propose a flexible cross-modal hashing approach, named Two-stage Asymmetric Similarity Preserving Hashing, TASPH for short, which can be applied to both unequal-length and equal-length retrieval scenarios. Specifically, in the first stage, TASPH designs a novel discrete asymmetric strategy to learn the modality-specific hash codes with varying lengths, enabling a flexible representation of heterogeneous data. Simultaneously, TASPH utilizes two semantic transformation matrices to establish the semantic correlations between varying hash codes. Different from most of the existing approaches that employ relaxation solutions, TASPH satisfies the discrete constraints without any relaxation. In the second stage, the learned semantic transformation matrices are employed to alleviate cross-modal heterogeneity, which guarantees that TASPH can learn more powerful hash functions to improve the discriminative ability of hash codes. Abundant experiments conducted on three benchmark datasets demonstrate encouraging results compared with the state-of-the-art approaches under different retrieval scenarios.

Hashing techniques have been extensively studied in cross-modal retrieval due to their advantages in high computational efficiency and low storage cost. However, existing methods unconsciously ignore the complementary information of multimodal data, thus failing to consider learning discriminative hash codes from the perspective of information complementarity while often involving time-consuming training overhead. To tackle the above issues, we propose an efficient discriminative hashing (EDH) with information complementarity consideration. Specifically, we reckon that multimodal features and their corresponding semantic labels describe heterogeneous data viewed from low-and high-level structures, which owns complementarity. To this end, low-level latent representation and high-level semantics representation are simply derived. Then, a joint learning strategy is formulated to simultaneously exploit the above two representations for generating discriminative hash codes, which is quite computationally efficient. Besides, EDH decomposes hash learning into two steps. To obtain powerful hash functions which are conductive to retrieval, a regularization term considering pairwise semantic similarity is introduced into hash functions learning. In addition, an efficient optimization algorithm is designed to solve the optimization problem in EDH. Extensive experiments conducted on benchmark datasets demonstrate the superiority of our EDH in terms of retrieval performance and training efficiency. The source code is available at https://github.com/hjf-hjf/EDH.

BACKGROUND: Identifying polymorphism clades on phylogenetic trees could help detect punctual mutations that are associated with viral functions. With visualization tools coloring the tree, it is easy to visually find clades where most sequences have the same polymorphism state. However, with the fast accumulation of viral sequences, a computational tool to automate this process is urgently needed. RESULTS: Here, by implementing a branch-and-bound-like search method, we developed an R package named sitePath to identify polymorphism clades automatically. Based on the identified polymorphism clades, fixed and parallel mutations could be inferred. Furthermore, sitePath also integrated visualization tools to generate figures of the calculated results. In an example with the influenza A virus H3N2 dataset, the detected fixed mutations coincide with antigenic shift mutations. The highly specificity and sensitivity of sitePath in finding fixed mutations were achieved for a range of parameters and different phylogenetic tree inference software. CONCLUSIONS: The result suggests that sitePath can identify polymorphism clades per site. The clustering of sequences on a phylogenetic tree can be used to infer fixed and parallel mutations. High-quality figures of the calculated results could also be generated by sitePath.

In general, existing cross-domain recognition methods mainly focus on changing the feature representation of data or modifying the classifier parameter and their efficiencies are indicated by the better performance. However, most existing methods do not simultaneously integrate them into a unified optimization objective for further improving the learning efficiency. In this article, we propose a novel cross-domain recognition algorithm framework by integrating both of them. Specifically, we reduce the discrepancies in both the conditional distribution and marginal distribution between different domains in order to learn a new feature representation which pulls the data from different domains closer on the whole. However, the data from different domains but the same class cannot interlace together enough and thus it is not reasonable to mix them for training a single classifier. To this end, we further propose to learn double classifiers on the respective domain and require that they dynamically approximate to each other during learning. This guarantees that we finally learn a suitable classifier from the double classifiers by using the strategy of classifier fusion. The experiments show that the proposed method outperforms over the state-of-the-art methods.

Cross-modal retrieval has attracted considerable attention for searching in large-scale multimedia databases because of its efficiency and effectiveness. As a powerful tool of data analysis, matrix factorization is commonly used to learn hash codes for cross-modal retrieval, but there are still many shortcomings. First, most of these methods only focus on preserving locality of data but they ignore other factors such as preserving reconstruction residual of data during matrix factorization. Second, the energy loss of data is not considered when the data of cross-modal are projected into a common semantic space. Third, the data of cross-modal are directly projected into a unified semantic space which is not reasonable since the data from different modalities have different properties. This article proposes a novel method called average approximate hashing (AAH) to address these problems by: 1) integrating the locality and residual preservation into a graph embedding framework by using the label information; 2) projecting data from different modalities into different semantic spaces and then making the two spaces approximate to each other so that a unified hash code can be obtained; and 3) introducing a principal component analysis (PCA)-like projection matrix into the graph embedding framework to guarantee that the projected data can preserve the main energy of data. AAH obtains the final hash codes by using an average approximate strategy, that is, using the mean of projected data of different modalities as the hash codes. Experiments on standard databases show that the proposed AAH outperforms several state-of-the-art cross-modal hashing methods.

This article proposes a novel data reconstruction method, called projective cross-reconstruction (PCR) for cross-domain recognition. The intrinsic philosophy behind PCR is that the data from different domains but with the same label have a strong correlation and thus they can be reconstructed with each other. To this end, we first rearrange the data of source and target domains to form two new cross-data matrices, and the data with the same label but from different domains can be arranged together. Then, we use two different projection matrices to project the new cross-data into two approximate subspaces and perform the cross-reconstruction without introducing any extra matrix as the reconstruction coefficient matrix. This guarantees that the data from different domains can be interlaced well and the data from different domains but sharing the same label can be aligned together. In doing so, the problem of cross-domain distribution mismatch is solved and a discriminative and transferrable feature representation can be obtained. Moreover, PCR integrates the classifier learning and feature representation learning into a unified framework so that these two tasks can be iteratively improved until the termination condition is met. Extensive experiments on six datasets validate the effectiveness of our proposed PCR, compared with the state-of-the-art methods.

The genus Culicoides includes biting midges, some of which are vectors for viruses that cause diseases in humans and animals. Knowledge of the roles of Culicoides in viral ecology is inadequate. We collected ~300 000 samples of Culicoides and mosquitoes in 15 representative regions within Yunnan, China. Using mosquitoes as reference vectors, we designed a comparative virome strategy to study the viral composition, diversity, hosts and spatiotemporal distribution of Culicoides. A map of viromes in Culicoides and mosquitoes in Yunan province, China, was constructed. At the same locations, Culicoides and mosquitoes usually share a similar viral diversity. At least 10 important pathogenic viruses were detected from Culicoides. Many novel viruses were discovered, including 21 segmented viruses of Flaviviridae, 180 viruses of Monjiviricetes and 130 viruses of Bunyavirales. The findings demonstrate that Culicoides is an important part of viral ecology and should be studied and monitored for potentially emerging viruses.

Accessory proteins play important roles in the interaction between coronaviruses and their hosts. Accordingly, a comprehensive study of the compositional diversity and evolutionary patterns of accessory proteins is critical to understanding the host adaptation and epidemic variation of coronaviruses. Here, we developed a standardized genome annotation tool for coronavirus (CoroAnnoter) by combining open reading frame prediction, transcription regulatory sequence recognition and homologous alignment. Using CoroAnnoter, we annotated 39 representative coronavirus strains to form a compositional profile for all of the accessary proteins. Large variations were observed in the number of accessory proteins of 1-10 for different coronaviruses, with SARS-CoV-2 and SARS-CoV having the most (9 and 10, respectively). The variation between SARS-CoV and SARS-CoV-2 accessory proteins could be traced back to related coronaviruses in other hosts. The genomic distribution of accessory proteins had significant intra-genus conservation and inter-genus diversity and could be grouped into 1, 4, 2 and 1 types for alpha-, beta-, gamma-, and delta-coronaviruses, respectively. Evolutionary analysis suggested that accessory proteins are more conservative locating before the N-terminal of proteins E and M (E-M), while they are more diverse after these proteins. Furthermore, comparison of virus-host interaction networks of SARS-CoV-2 and SARS-CoV accessory proteins showed that they share multiple antiviral signaling pathways, those involved in the apoptotic process, viral life cycle and response to oxidative stress. In summary, our study provides a tool for coronavirus genome annotation and builds a comprehensive profile for coronavirus accessory proteins covering their composition, classification, evolutionary pattern and host interaction.

This paper addresses two fundamental problems: 1) learning discriminative model parameters and 2) avoiding over-fitting, which often occurs in regression-based classification tasks. We formulate these two problems in terms of relaxing both the strict binary label matrix and graph regularization term into more flexible forms so that the margins between different classes are enlarged as much as possible and the problem of over-fitting is avoided to some extent. This task is accomplished by the proposed double relaxed regression (DRR) method. The convex problem of DRR is solved efficiently with an iterative procedure. Extensive experiments on synthetic and real world image data sets demonstrate the effectiveness of the proposed method in terms of both classification accuracy and running time.

A major assumption in data mining and machine learning is that the training set and test set come from the same domain. They share the same feature space and have the same distribution. However, in many real-world applications, the training set and test set usually come from different domains. Thus, there might be negative similarities between different domains so that the negative transfer problem caused by negative similarity may happen. In this paper, we propose a novel method named active transfer learning (ATL) to solve the above problem. Specifically, the orthogonal projection matrix and the weight coefficient vector are introduced to extend maximum mean discrepancy (MMD) so that it can minimize MMD and simultaneously eliminate the negative transfer. To find the informative and discriminative subsets from the source domain, we then propose an information diversity term by using the local geometric structure information of the source samples. Besides, by using the label information of source samples, our method can guarantee the selected subsets as discriminative as possible. Finally, to efficiently implement the proposed method, an alternating optimization approach, which is based on the alternating direction method of multipliers (ADMM), is designed to solve the optimization problem. To demonstrate the effectiveness of the proposed ATL model, experiments are conducted on five real-world data sets. The experimental results show the superiority of our method over the state-of-the-art methods.

Graph based classification methods have been widely applied in the fields of computer vision and machine learning. The quality of the graph highly affects the performance of these methods. The same object is commonly represented by different features, i.e., multi-view features, which leads to multiple graphs corresponding to different features in multi-view learning. However, what kind of graph is important for the task is unknown in advance. Moreover, existing multi-view learning methods become weak in dealing with noisy graphs when the data is corrupted by the noise. In this paper, we address this problem by observing that the noise of each graph has specific structure. Then, based on this observation we propose a robust multi-view graph recovery (RMGR) method in which the specific structure is used to clean the multiple input noisy graphs and these cleaned graphs are simultaneously aggregated into a consensus graph by adaptively assigning great weighted coefficients for important graphs. To make the consensus graph suit classification, the clustering structure is introduced to restrain the rank of Laplacian matrix of the consensus graph such that the number of its connected components is equal to that of clustering. In doing so, the graph is adaptively adjusted during optimization to more accurately partition data. The optimization problem is solved by proposed the iterative update algorithm. Extensive experiments on synthetic and several benchmark data sets show the effectiveness of the proposed method.

In this paper, a novel least square regression method, named group low-rank representation-based discriminant linear regression (GLRRDLR), is proposed for multi-class classification. Unlike the conventional linear regression methods, the proposed method aims to learn a more discriminative projection. Specially, two main techniques are adopted to improve the discriminability of the projection. The first approach is to make the transformed samples locate in their own subspace by introducing a group low-rank constraint to the model, such that the distance between samples from the same class can be decreased greatly. The second approach is to simultaneously learn a discriminative target matrix for regression. The extensive experimental results show that the proposed method performs much better than the state-of-the-art methods, which proves the effectiveness of the above two approaches in improving the discriminability of the projection.

Dictionary learning plays a significant role in the field of machine learning. Existing works mainly focus on learning dictionary from a single domain. In this paper, we propose a novel projective double reconstructions (PDR) based dictionary learning algorithm for cross-domain recognition. Owing the distribution discrepancy between different domains, the label information is hard utilized for improving discriminability of dictionary fully. Thus, we propose a more flexible label consistent term and associate it with each dictionary item, which makes the reconstruction coefficients have more discriminability as much as possible. Due to the intrinsic correlation between cross-domain data, the data should be reconstructed with each other. Based on this consideration, we further propose a projective double reconstructions scheme to guarantee that the learned dictionary has the abilities of data itself reconstruction and data crossreconstruction. This also guarantees that the data from different domains can be boosted mutually for obtaining a good data alignment, making the learned dictionary have more transferability. We integrate the double reconstructions, label consistency constraint and classifier learning into a unified objective and its solution can be obtained by proposed optimization algorithm that is more efficient than the conventional l1 optimization based dictionary learning methods. The experiments show that the proposed PDR not only greatly reduces the time complexity for both training and testing, but also outperforms over the stateof- the-art methods.

Subspace learning based transfer learning methods commonly find a common subspace where the discrepancy of the source and target domains is reduced. The final classification is also performed in such subspace. However, the minimum discrepancy does not guarantee the best classification performance and thus the common subspace may be not the best discriminative. In this paper, we propose a latent elastic-net transfer learning (LET) method by simultaneously learning a latent subspace and a discriminative subspace. Specifically, the data from different domains can be well interlaced in the latent subspace by minimizing Maximum Mean Discrepancy (MMD). Since the latent subspace decouples inputs and outputs and, thus a more compact data representation is obtained for discriminative subspace learning. Based on the latent subspace, we further propose a low-rank constraint based matrix elastic-net regression to learn another subspace in which the intrinsic intra-class structure correlations of data from different domains is well captured. In doing so, a better discriminative alignment is guaranteed and thus LET finally learns another discriminative subspace for classification. Experiments on visual domains adaptation tasks show the superiority of the proposed LET method.

Linear discriminant analysis (LDA) has been widely used as the technique of feature exaction. However, LDA may be invalid to address the data from different domains. The reasons are as follows: 1) the distribution discrepancy of data may disturb the linear transformation matrix so that it cannot extract the most discriminative feature and 2) the original design of LDA does not consider the unlabeled data so that the unlabeled data cannot take part in the training process for further improving the performance of LDA. To address these problems, in this brief, we propose a novel transferable LDA (TLDA) method to extend LDA into the scenario in which the data have different probability distributions. The whole learning process of TLDA is driven by the philosophy that the data from the same subspace have a low-rank structure. The matrix rank in TLDA is the key learning criterion to conduct local and global linear transformations for restoring the low-rank structure of data from different distributions and enlarging the distances among different subspaces. In doing so, the variations of distribution discrepancy within the same subspace can be reduced, i.e., data can be aligned well and the maximally separated structure can be achieved for the data from different subspaces. A simple projected subgradient-based method is proposed to optimize the objective of TLDA, and a strict theory proof is provided to guarantee a quick convergence. The experimental evaluation on public data sets demonstrates that our TLDA can achieve better classification performance and outperform the state-of-the-art methods.

Preserving global and local structures during projection learning is very important for feature extraction. Although various methods have been proposed for this goal, they commonly introduce an extra graph regularization term and the corresponding regularization parameter that needs to be tuned. However, tuning the parameter manually not only is time-consuming, but also is difficult to find the optimal value to obtain a satisfactory performance. This greatly limits their applications. Besides, projections learned by many methods do not have good interpretability and their performances are commonly sensitive to the value of the selected feature dimension. To solve the above problems, a novel method named low-rank preserving projection via graph regularized reconstruction (LRPP_GRR) is proposed. In particular, LRPP_GRR imposes the graph constraint on the reconstruction error of data instead of introducing the extra regularization term to capture the local structure of data, which can greatly reduce the complexity of the model. Meanwhile, a low-rank reconstruction term is exploited to preserve the global structure of data. To improve the interpretability of the learned projection, a sparse term with${l_{2,1}}$norm is imposed on the projection. Furthermore, we introduce an orthogonal reconstruction constraint to make the learned projection hold main energy of data, which enables LRPP_GRR to be more flexible in the selection of feature dimension. Extensive experimental results show the proposed method can obtain competitive performance with other state-of-the-art methods.

In this paper, we propose a unified model called flexible affinity matrix learning (FAML) for unsupervised and semisupervised classification by exploiting both the relationship among data and the clustering structure simultaneously. To capture the relationship among data, we exploit the self-expressiveness property of data to learn a structured matrix in which the structures are induced by different norms. A rank constraint is imposed on the Laplacian matrix of the desired affinity matrix, so that the connected components of data are exactly equal to the cluster number. Thus, the clustering structure is explicit in the learned affinity matrix. By making the estimated affinity matrix approximate the structured matrix during the learning procedure, FAML allows the affinity matrix itself to be adaptively adjusted such that the learned affinity matrix can well capture both the relationship among data and the clustering structure. Thus, FAML has the potential to perform better than other related methods. We derive optimization algorithms to solve the corresponding problems. Extensive unsupervised and semisupervised classification experiments on both synthetic data and real-world benchmark data sets show that the proposed FAML consistently outperforms the state-of-the-art methods.

Feature extraction plays a significant role in pattern recognition. Recently, many representation-based feature extraction methods have been proposed and achieved successes in many applications. As an excellent unsupervised feature extraction method, latent low-rank representation (LatLRR) has shown its power in extracting salient features. However, LatLRR has the following three disadvantages: 1) the dimension of features obtained using LatLRR cannot be reduced, which is not preferred in feature extraction; 2) two low-rank matrices are separately learned so that the overall optimality may not be guaranteed; and 3) LatLRR is an unsupervised method, which by far has not been extended to the supervised scenario. To this end, in this paper, we first propose to use two different matrices to approximate the low-rank projection in LatLRR so that the dimension of obtained features can be reduced, which is more flexible than original LatLRR. Then, we treat the two low-rank matrices in LatLRR as a whole in the process of learning. In this way, they can be boosted mutually so that the obtained projection can extract more discriminative features. Finally, we extend LatLRR to the supervised scenario by integrating feature extraction with the ridge regression. Thus, the process of feature extraction is closely related to the classification so that the extracted features are discriminative. Extensive experiments are conducted on different databases for unsupervised and supervised feature extraction, and very encouraging results are achieved in comparison with many state-of-the-arts methods.

BACKGROUND: Pathogen detection in clinical samples based on 16S metagenomic sequencing technology in microbiology laboratories is an important strategy for clinical diagnosis, public health surveillance, and investigations of outbreaks. However, the implementation of the technology is limited by its accuracy and the time required for bioinformatics analysis. Therefore, a simple, standardized, and rapid analysis pipeline from the receipt of clinical samples to the generation of a test report is needed to increase the use of metagenomic analyses in clinical settings. RESULTS: We developed a comprehensive bioinformatics analysis pipeline for the identification of pathogens in clinical samples based on 16S metagenomic sequencing data, named 16SPIP. This pipeline offers two analysis modes (fast and sensitive mode) for the rapid conversion of clinical 16S metagenomic data to test reports for pathogen detection. The pipeline includes tools for data conversion, quality control, merging of paired-end reads, alignment, and pathogen identification. We validated the feasibility and accuracy of the pipeline using a combination of culture and whole-genome shotgun (WGS) metagenomic analyses. CONCLUSIONS: 16SPIP may be effective for the analysis of 16S metagenomic sequencing data for real-time, rapid, and unbiased pathogen detection in clinical samples.

Full bidirectional switches two stage matrix converter (FBS-TSMC), which can be effectively used in critical applications, is presented. Comparing with traditional two stage matrix converter, it adds six additional switches to inverter stage as redundancy hot backup. Meanwhile, its bidirectional switches structure in inverter stage makes it possible to reverse dc bus voltage polarity. The above two features provide FBS-TSMC fault-tolerant capacity in the presence of faults. From the view of modulation, this paper proposes two fault-tolerant strategies for FBS-TSMC to deal with single switch short circuit failure and bidirectional switch open circuit failure in inverter stage respectively. By utilizing the characteristics of fault switch and increasing additional switch times in healthy phases, the proposed fault-tolerant strategies can ensure FBS-TSMC's satisfactory performance without modifying the control technique. A simulation model of FBS-TSMC has been built in Matlab/Simulink and the verification of the proposed fault-tolerant modulation strategy is confirmed using extensive simulation results.

In order to detect open-circuit fault in rectifier stage, a fault diagnosis method applied to indirect matrix converter (IMC) is proposed in this paper. Firstly, the theory and feature of space vector modulation (SVM) are introduced. Then, detailed circuit analysis of IMC under fault condition is fully studied, and from the view of energy transfer, the influence of open-circuit fault on output voltage is analysed. Based on the circuit analysis, this paper presents a fault diagnosis technology to detect and locate faulty switch by combining the corresponding relationship between the rectifier stage sector and bidirectional switch. The proposed method has the advantages of being easy to understand, locating faulty switch accurately, requiring no additional sensor in dc-link side. Simulation results are presented to verify the proposed fault detection technology.

There is an increasing interest in two-stage four-leg matrix converter (3×4TSMC) which provides a neutral connection for potential imbalanced loads. However, the fourth leg will worsen its operation performance unexpectedly, even if connected to the balanced loads. By means of analysis and calculation, it is found that the input filter capacitor voltage fluctuation will transfer to the output in the process of modulation, generating input frequency related harmonic in neutral current and distorting output voltage waveforms. To suppress this low-frequency harmonic, an improved time-varying repetitive control (TRC) scheme, which uses linear interpolation other than changing sampling period to obtain satisfactory harmonic rejection in frequency conversion system, is proposed. Its specific hardware implementation is presented on the heels. The correctness of harmonic analysis and validity of the control method are verified by experiments.

Variable speed constant frequency (VSCF) generation system based on a two-stage matrix converter (TSMC) has advantages in efficiency, power density, reliability and lifetime, thus earning a potential application prospect in aero power supply system. A proper input filter is essential for the TSMC to eliminate high-frequency harmonics and improve input-side performance. However, the design of input filter gets rather difficult, because it is hard to obtain low total harmonic distortion (THD) of input current and high input power factor simultaneously under the high input frequency in aero power system. In aim to solve this problem, a set of design principles is proposed to ensure that the TSMC meets related requirements throughout a given range of load power. The parameter limitations of input filter with different switching frequencies and output power range are summarized, which helps simplify the filter-design process. The analysis results prove that the available parameter range of the input filter turns out to be null, if switching frequency is too low or load power range is too wide. The feasibility and validity of input filter design results is verified via simulation.