Research on Microwave Pyrolysis Recovery and Reuse Performance of Carbon Fiber Composites.

Carbon fiber-reinforced resin matrix composites find extensive applications across various industries. However, their widespread use also generates significant waste, leading to resource depletion and environmental concerns. Studying the production of composite materials using recovered carbon fiber is imperative to mitigate the environmental impact associated with waste from carbon fiber-reinforced resin matrix composites and optimize resource utilization. In this study, carbon fiber was reclaimed using the microwave pyrolysis-oxidation process. The reclaimed carbon fiber underwent a cutting process to produce shorter carbon fibers tailored to specific requirements, which were then used to fabricate composite plates reinforced with epoxy resin. The mechanical characteristics of the composite were analyzed, along with SEM, XPS, infrared, Raman, and contact angle analyses conducted on the recovered carbon fiber. The test findings suggested minimal variation in the surface morphology of the recovered carbon fiber materials. Post-recovery, an increase in the quantity of oxygen-containing functional groups was observed on the carbon fiber surface. Additionally, the contact angle between the carbon fiber surface and the epoxy adhesive decreased. The mechanical properties of the composite produced from the recovered carbon fiber decreased, including the impact strength, tensile strength, and bending strength, with the impact strength dropping by 24.14%, tensile strength by 15.94%, and bending strength by 8.24%, while maintaining overall reusability, thus paving the way for the comprehensive utilization of carbon fiber resources.

Bacterial community succession and the enrichment of antibiotic resistance genes on microplastics in an oyster farm.

Microplastics can be colonized by microorganisms and form plastisphere. However, knowledge of bacterial community succession and the enrichment of antibiotic resistance genes (ARGs) and pathogens on microplastics in aquaculture environments is limited. Here, we conducted a 30-day continuous exposure experiment at an oyster farm. Results showed that the alpha-diversity of communities on most microplastics continuously increased and was higher than in planktonic communities after 14 days. Microplastics could selectively enrich certain bacteria from water which can live a sessile lifestyle and promote colonization by other bacteria. The composition and function of plastisphere communities were distinct from those in the surrounding water and influenced by polymer type and exposure time. Microplastics can enrich ARGs (sul1, qnrS and blaTEM) and harbor potential pathogens (e.g., Pseudomonas aeruginosa). Therefore, microplastic pollution may pose a critical threat to aquaculture ecosystems and human health. Our study provides further insight into the ecological risks of microplastics.

Distribution and correlation between antibiotic resistance genes and host-associated markers before and after swine fever in the longjiang watershed.

Antibiotic resistance genes (ARGs) are abundantly shed in feces. Thus, it is crucial to identify their host sources so that ARG pollution can be effectively mitigated and aquatic ecosystems can be properly conserved. Here, spatiotemporal variations and sources of ARGs in the Longjiang watershed of South China were investigated by linking them with microbial source tracker (MST) indicators. The most frequently detected ARGs (>90%) were sulI, sulII, blaTEM, tetW, ermF, and the mobile element intI1. Spatial distribution analyses showed that tributaries contributed significantly more sulI, sulII, and ermF contamination to the Longjiang watershed than the main channel. MST indicator analysis revealed that the Longjiang watershed was contaminated mainly by human fecal pollution. Livestock- and poultry-associated fecal pollution significantly declined after the swine fever outbreak. The occurrence of most ARGs is largely explained by human fecal pollution. In contrast, pig fecal pollution might account for the prevalence of tetO. Moreover, combined human-pig fecal pollution contributed to the observed blaNDM-1 distribution in the Longjiang watershed. Subsequent analysis of the characteristics of MST markers disclosed that the relatively lower specificities of BacHum and Rum-2-Bac may lead to inaccurate results of tracking ARG pollution source. The present study determined spatiotemporal variations and ARG origins in the Longjiang watershed by combining MST markers. It also underscored the necessity of using multiple MST markers simultaneously to identify and characterize ARG pollution sources accurately.

Pulsed infrared stimulation evoked electrical potential in mouse vestibular system.

To improve accuracy of VsEP and avoid the inherent limitation of mechanical vibration, we designed an infrared optical stimulation approach to stimulate mouse vestibular system and measured the evoked potential. IR pulses (1871 nm, 30 pps and 100 µs pulse width) were delivered to mice with different vestibular dysfunction levels and the evoked potential was recorded. The result suggests that the amplitude and latency of the IR-evoked potential (IR-VsEP) were significantly associated with vestibular function integrity. Immunofluorescence staining confirmed that magnitude of IR-VsEP decreased was consistent with the loss of HCs. Micro-CT imaging revealed that the optical fiber was orientating towards the vestibular system. Taken together, we found that: 1) IR stimulation can generate VsEP evoked potential in vestibular system (IR-VsEP), which can be potentially used for vestibular function evaluation; 2) intact HCs and fully functional synaptic transmission are crucial for efficient IR-induced vestibular system stimulation.

Structured AutoEncoders for Subspace Clustering.

Existing subspace clustering methods typically employ shallow models to estimate underlying subspaces of unlabeled data points and cluster them into corresponding groups. However, due to the limited representative capacity of the employed shallow models, those methods may fail in handling realistic data without the linear subspace structure. To address this issue, we propose a novel subspace clustering approach by introducing a new deep model-Structured AutoEncoder (StructAE). The StructAE learns a set of explicit transformations to progressively map input data points into nonlinear latent spaces while preserving the local and global subspace structure. In particular, to preserve local structure, the StructAE learns representations for each data point by minimizing reconstruction error w.r.t. itself. To preserve global structure, the StructAE incorporates a prior structured information by encouraging the learned representation to preserve specified reconstruction patterns over the entire data set. To the best of our knowledge, StructAE is one of first deep subspace clustering approaches. Extensive experiments show that the proposed StructAE significantly outperforms 15 state-of-the-art subspace clustering approaches in terms of five evaluation metrics.

Robust Kernel Low-Rank Representation.

Recently, low-rank representation (LRR) has shown promising performance in many real-world applications such as face clustering. However, LRR may not achieve satisfactory results when dealing with the data from nonlinear subspaces, since it is originally designed to handle the data from linear subspaces in the input space. Meanwhile, the kernel-based methods deal with the nonlinear data by mapping it from the original input space to a new feature space through a kernel-induced mapping. To effectively cope with the nonlinear data, we first propose the kernelized version of LRR in the clean data case. We also present a closed-form solution for the resultant optimization problem. Moreover, to handle corrupted data, we propose the robust kernel LRR (RKLRR) approach, and develop an efficient optimization algorithm to solve it based on the alternating direction method. In particular, we show that both the subproblems in our optimization algorithm can be efficiently and exactly solved, and it is guaranteed to obtain a globally optimal solution. Besides, our proposed algorithm can also solve the original LRR problem, which is a special case of our RKLRR when using the linear kernel. In addition, based on our new optimization technique, the kernelization of some variants of LRR can be similarly achieved. Comprehensive experiments on synthetic data sets and real-world data sets clearly demonstrate the efficiency of our algorithm, as well as the effectiveness of RKLRR and the kernelization of two variants of LRR.

A Unified Framework for Representation-Based Subspace Clustering of Out-of-Sample and Large-Scale Data.

Under the framework of spectral clustering, the key of subspace clustering is building a similarity graph, which describes the neighborhood relations among data points. Some recent works build the graph using sparse, low-rank, and l2 -norm-based representation, and have achieved the state-of-the-art performance. However, these methods have suffered from the following two limitations. First, the time complexities of these methods are at least proportional to the cube of the data size, which make those methods inefficient for solving the large-scale problems. Second, they cannot cope with the out-of-sample data that are not used to construct the similarity graph. To cluster each out-of-sample datum, the methods have to recalculate the similarity graph and the cluster membership of the whole data set. In this paper, we propose a unified framework that makes the representation-based subspace clustering algorithms feasible to cluster both the out-of-sample and the large-scale data. Under our framework, the large-scale problem is tackled by converting it as the out-of-sample problem in the manner of sampling, clustering, coding, and classifying. Furthermore, we give an estimation for the error bounds by treating each subspace as a point in a hyperspace. Extensive experimental results on various benchmark data sets show that our methods outperform several recently proposed scalable methods in clustering a large-scale data set.

Automatic face naming by learning discriminative affinity matrices from weakly labeled images.

Given a collection of images, where each image contains several faces and is associated with a few names in the corresponding caption, the goal of face naming is to infer the correct name for each face. In this paper, we propose two new methods to effectively solve this problem by learning two discriminative affinity matrices from these weakly labeled images. We first propose a new method called regularized low-rank representation by effectively utilizing weakly supervised information to learn a low-rank reconstruction coefficient matrix while exploring multiple subspace structures of the data. Specifically, by introducing a specially designed regularizer to the low-rank representation method, we penalize the corresponding reconstruction coefficients related to the situations where a face is reconstructed by using face images from other subjects or by using itself. With the inferred reconstruction coefficient matrix, a discriminative affinity matrix can be obtained. Moreover, we also develop a new distance metric learning method called ambiguously supervised structural metric learning by using weakly supervised information to seek a discriminative distance metric. Hence, another discriminative affinity matrix can be obtained using the similarity matrix (i.e., the kernel matrix) based on the Mahalanobis distances of the data. Observing that these two affinity matrices contain complementary information, we further combine them to obtain a fused affinity matrix, based on which we develop a new iterative scheme to infer the name of each face. Comprehensive experiments demonstrate the effectiveness of our approach.