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Aiming at the problems of high similarity of T-type inverter open-circuit fault features, cumbersome manual extraction of fault features, and the inability of one-dimensional convolutional neural network to fully play the role of feature extraction. In this paper, an end-to-end fault diagnosis method is proposed, which is based on Gramian Angular Summation Field and improved AlexNet network. The fault results can be diagnosed by collecting only the single-phase line voltage. Firstly, the collected one-dimensional timing signal is mapped into two-dimensional images through the Gram summation angle field algorithm. Then feature extraction is performed by the improved AlexNet to take advantage of its ability to extract image features. Finally, the fault diagnosis result is output by the Softmax layer. Simulation experiments show that the method proposed in this paper can automatically extract features that are helpful for fault identification from raw data. The fault diagnosis rate of this model is as high as 99.72%, and it can diagnose not only a single fault, but also multiple faults in different phases. Compared with other methods, the proposed method shows a better fault feature extraction effect and higher fault diagnosis accuracy.

Inspired by the layered structure, light absorption, and charge carrier pathway of chloroplast thylakoids in natural photosynthesis, we propose a novel artificial photosynthesis platform, which is composed of layered structured vaterite as the scaffold with gold nanoparticles (AuNPs), photosensitizer eosin Y (EY), and redox enzyme L-glutamate dehydrogenase (GDH) as the functional components. The EY exhibited significantly enhanced light absorption and charge carrier generation due to the localized surface plasmon resonance (LSPR) around the AuNPs and light refraction within the layers. This artificial photosynthesis platform can regenerate reduced nicotinamide adenine dinucleotide (NADH) under visible light and promote the rapid conversion of α-ketoglutarate to L-glutamate (0.453 Mm/h). The excellent biocompatibility of layered vaterite significantly enhances the resistance of GDH to harsh conditions, including high pH (pH = 10) and elevated temperatures (37–57 °C).

In aircraft feature detection, the difficulty of acquiring Synthetic Aperture Radar (SAR) images leads to the scarcity of some types of aircraft samples, and the high privacy makes the personal sample set have the characteristics of data silos. Existing data enhancement methods can alleviate the problem of data scarcity through feature reuse, but they are still powerless for data that are not involved in local training. To solve this problem, a new federated learning framework was proposed to solve the problem of data scarcity and data silos through multi-client joint training and model aggregation. The commonly used federal average algorithm is not effective for aircraft detection with unbalanced samples, so a federal distribution average deviation (FedDAD) algorithm, which is more suitable for aircraft detection in SAR images, was designed. Based on label distribution and client model quality, the contribution ratio of each client parameter is adaptively adjusted to optimize the global model. Client models trained through federated cooperation have an advantage in detecting aircraft with unknown scenarios or attitudes while remaining sensitive to local datasets. Based on the YOLOv5s algorithm, the feasibility of federated learning was verified on SAR image aircraft detection datasets and the portability of the FedDAD algorithm on public datasets. In tests based on the YOLOv5s algorithm, FedDAD outperformed FedAvg’s mAP0.5–0.95 on the total test set of two SAR image aircraft detection and far outperformed the local centralized training model.

The present study provides a comprehensive overview of the Bacillus velezensis strain Htq6, and its potential applications in plant disease control. Htq6 is an endophytic bacterium derived from walnut, which was found to possess a strong inhibitory effect on a wide range of plant pathogenic microorganisms and was identified as a good plant disease control agent. The entire genome of the Bacillus velezensis Htq6 was sequenced, and a comparative genomic analysis was conducted with various Bacillus species in order to better understand the mechanism of the strain’s biological control. At the same time, a new classification result was presented. Additionally, transcriptome analysis was performed to explore the response mechanism of tomato gray mold fungus after treatment with the fermentation liquid of Bacillus velezensis Htq6. The study analyzed the distribution of various secondary metabolite gene clusters in the Bacillus model strains and employed RNA-Seq technology to obtain transcriptome expression profiles. Furthermore, the cell wall, cell membrane, and antioxidant-related genes of Botrytis cinerea were analyzed, providing insight into the antibacterial mechanism of biocontrol bacteria and the stress response mechanism of Botrytis cinerea. The results of the research are promising, and could potentially lead to the development of an effective biocontrol agent for the prevention and control of various plant diseases.

Images taken in extremely low light suffer from various problems such as heavy noise, blur, and color distortion. Assuming the low-light images contain a good representation of the scene content, current enhancement methods focus on finding a suitable illumination adjustment but often fail to deal with heavy noise and color distortion. Recently, some works try to suppress noise and reconstruct low-light images from raw data. But these works apply a network instead of an image signal processing pipeline (ISP) to map the raw data to enhanced results which leads to heavy learning burden for the network and get unsatisfactory results. In order to remove heavy noise, correct color bias and enhance details more effectively, we propose a two-stage Low Light Image Signal Processing Network named LLISP. The design of our network is inspired by the traditional ISP: processing the images in multiple stages according to the attributes of different tasks. In the first stage, a simple denoising module is introduced to reduce heavy noise. In the second stage, we propose a two-branch network to reconstruct the low-light images and enhance texture details. One branch aims at correcting color distortion and restoring image content, while another branch focuses on recovering realistic texture. Experimental results demonstrate that the proposed method can reconstruct high-quality images from low-light raw data and replace the traditional ISP.

The third generation of Audio Video coding Standard (AVS3) is an emerging video coding standard that surpasses High Efficiency Video Coding (HEVC). AVS3 allows flexible block subdivision by applying Quad-Tree (QT), Binary-Tree (BT) plus Extend Quad-Tree (EQT) partition structure, while the increased flexibility comes at the cost of enormous coding complexity. In this paper, an ingenious early termination mechanism is proposed to skip unnecessary exhaustive searches of the whole tree branches. We carry out a series of restrictive measures based on Coding Unit (CU) size and iteration status to make the sophisticated EQT split mode concentrate more on small CUs with complex texture structure. Historical QTBT partition information is also adopted as an important factor to skip EQT split mode in advance. Meanwhile, a fast CU partition algorithm based on the gradient is proposed to skip horizontal or vertical BT/EQT partition of CUs with prominent texture structure in another direction, in which early termination can be directly conducted in homogenous areas at the same time. Extensive experiments demonstrate that the proposed method can save 43% encoding time with only 0.53% BDBR increase on average under All Intra(AI) configuration, which outperforms the preexisting fast algorithms.

Background: To probe into the influence of evidence-based nursing (EBN) on clinical recovery and prognosis of patients with arrhythmia after acute myocardial infarction (AMI). Methods: Totally, 240 AMI patients with arrhythmia treated in Taizhou People's Hospital（Jiangsu, China） from July 2019 to December 2020 were collected and randomly divided into the study group (n = 120) and control group (n = 120). The control group was received routine nursing, while the study group carried out EBN. The following indicators were evaluated and compared between the two groups: length of hospital stay, symptom disappearance time, cardiac function, psychological status, and incidence of adverse events after 6 months of follow-up were. Results: Compared to the control group, the length of hospital stay, symptom disappearance time, LVEF (left ventricular ejection fraction), LVEDD (left ventricular end-diastolic diameter), SAS (self-rating anxiety scale) score and SDS (self-rating depression scale) score in the study group were significant improves (P < 0.05), and the incidence of adverse events after 6-month follow-up in the study group was also significantly lower than that in the control group (P < 0.05). Conclusion: EBN intervention for AMI patients with arrhythmia can significantly improve the length of hospital stay and symptom disappearance time, adjust cardiac function and psychological status, and reduce the incidence of adverse events.

In this paper, a configuration optimization method for a diesel hybrid energy system has been proposed. The method is based on a fuzzy artificial bee colony optimization mechanism that considers the power supply characteristics of an isolated hybrid energy system. First, a mathematical model based on a diesel hybrid energy system and a configuration optimization objective function that considers cost and load shortages is constructed. Then, an objective function is optimized in combination with fuzzy rules and artificial swarming algorithms to obtain a system configuration that meets multiple objective requirements. Finally, the system configurations of three hybrid modes of photovoltaic (PV)/energy storage/diesel, wind power/energy storage/diesel, and PV/wind power/energy storage/diesel are compared, and the pollutant emissions of the different systems are analyzed. Experimental results show that the new method exhibits good configuration optimization and performance, and it is a practical approach for solving the problem of microgrid system configuration.

System identification plays an important role in improving the structure and parameters of a system, but there are many problems encountered in actual operation. The identification of dynamic systems is not as simple as it is for static systems; thus, choosing effective model structures and parameters is the key to solving this problem. This paper proposes a novel algorithm based on a combination of a broad learning system (BLS) and particle swarm optimization (PSO) to identify nonlinear dynamical systems. The proposed method first uses the dimension expansion of the data set as the input of the BLS and then optimizes the model weight by the PSO algorithm. To verify the effectiveness of our proposal, we use four second-order systems for simulation experiments. The simulation results clearly show the efficiency and anti-interference ability of the proposed method.

Undesirable side effects and multidrug tolerance are the main holdbacks to the treatment of cancer in conventional chemotherapy. Fortunately, targeted drug delivery can improve the enrichment of drugs at the target site and reduce toxicity to normal tissues and cells. A targeted drug delivery system is usually composed of a nanocarrier and a targeting component. The targeting component is called a “ligand”. Aptamers have high target affinity and specificity, which are identified as attractive and promising ligands. Therefore, aptamers have potential application in the development of smart targeting systems. For instance, aptamers are able to efficiently recognize tumor markers such as nucleolin, mucin, and epidermal growth factor receptor (EGFR). Besides, aptamers can also identify glycoproteins on the surface of tumor cells. Thus, the aptamer-mediated targeted drug delivery system has received extensive attention in the application of cancer therapy. This article reviews the application of aptamers as smart ligands for targeted drug delivery in cancer therapy. Special interest is focused on aptamers as smart ligands, aptamer-conjugated nanocarriers, aptamer targeting strategy for tumor microenvironment (TME), and aptamers that are specified to crucial cancer biomarkers for targeted drug delivery.

Abstract Background In recent years, with the development of high-throughput genome sequencing technologies, a large amount of genome data has been generated, which has caused widespread concern about data storage and transmission costs. However, how to effectively compression genome sequences data remains an unsolved problem. Results In this paper, we propose a compression method using machine learning techniques (DeepDNA), for compressing human mitochondrial genome data. The experimental results show the effectiveness of our proposed method compared with other on the human mitochondrial genome data. Conclusions The compression method we proposed can be classified as non-reference based method, but the compression effect is comparable to that of reference based methods. Moreover, our method not only have a well compression results in the population genome with large redundancy, but also in the single genome with small redundancy. The codes of DeepDNA are available at https://github.com/rongjiewang/DeepDNA.

Abstract Background Numerous essential algorithms and methods, including entropy-based quantitative methods, have been developed to analyze complex DNA sequences since the last decade. Exons and introns are the most notable components of DNA and their identification and prediction are always the focus of state-of-the-art research. Results In this study, we designed an integrated entropy-based analysis approach, which involves modified topological entropy calculation, genomic signal processing (GSP) method and singular value decomposition (SVD), to investigate exons and introns in DNA sequences. We optimized and implemented the topological entropy and the generalized topological entropy to calculate the complexity of DNA sequences, highlighting the characteristics of repetition sequences. By comparing digitalizing entropy values of exons and introns, we observed that they are significantly different. After we converted DNA data to numerical topological entropy value, we applied SVD method to effectively investigate exon and intron regions on a single gene sequence. Additionally, several genes across five species are used for exon predictions. Conclusions Our approach not only helps to explore the complexity of DNA sequence and its functional elements, but also provides an entropy-based GSP method to analyze exon and intron regions. Our work is feasible across different species and extendable to analyze other components in both coding and noncoding region of DNA sequences.

Hydrogels of flexibility, strength, and conductivity have demonstrated broad applications in wearable electronics and soft robotics. However, it is still a challenge to fabricate conductive hydrogels with high strength massively and economically. Herein, a simple strategy is proposed to design a strong ionically conductive hydrogel. This ion-conducting hydrogel was obtained under the synergistic action by salting out the frozen mixture of polyvinyl alcohol (PVA) and graphene oxide (GO) using a high concentration of sodium chloride solution. The developed hydrogel containing only 5 wt% PVA manifests good tensile stress (65 kPa) and elongation (180%). Meanwhile, the PVA matrix doped with a small amount of GO formed uniformly porous ion channels after salting out, endowed the PVA/GO hydrogel with excellent ionic conductivity (up to 3.38 S m−1). Therefore, the fabricated PVA/GO hydrogel, anticipated for a strain sensor, exhibits good sensitivity (Gauge factor = 2.05 at 100% strain), satisfying working stability (stably cycled for 10 min), and excellent recognition ability. This facile method to prepare conductive hydrogels displays translational potential in flexible electronics for engineering applications.

In order to realize the unsupervised extraction and identification of fault features in power electronic circuits, we proposed a fault diagnosis method based on sparse autoencoder (SAE) and broad learning system (BLS). Firstly, the feature is extracted by the sparse autoencoder, and the fault samples and feature vectors are combined as the input of the broad learning system. The broad learning system is trained based on the error precision step update method, and the system is used to the fault type identification. The simulation results of the thyristor fault diagnosis of the three-phase bridge rectifier circuit show that the method is effective and has better performance than other traditional methods.

Dramatic increases in data produced by next-generation sequencing (NGS) technologies demand data compression tools for saving storage space. However, effective and efficient data compression for genome sequencing data has remained an unresolved challenge in NGS data studies. In this paper, we propose a novel alignment-free and reference-free compression method, BdBG, which is the first to compress genome sequencing data with dynamic de Bruijn graphs based on the data after bucketing. Compared with existing de Bruijn graph methods, BdBG only stored a list of bucket indexes and bifurcations for the raw read sequences, and this feature can effectively reduce storage space. Experimental results on several genome sequencing datasets show the effectiveness of BdBG over three state-of-the-art methods. BdBG is written in python and it is an open source software distributed under the MIT license, available for download at https://github.com/rongjiewang/BdBG.

The identification of values of solar cell parameters is of great interest for evaluating solar cell performances. The algorithm of an artificial bee colony was used to extract model parameters of solar cells from current-voltage characteristics. Firstly, the best-so-for mechanism was introduced to the original artificial bee colony. Then, a method was proposed to identify parameters for a single diode model and double diode model using this improved artificial bee colony. Experimental results clearly demonstrate the effectiveness of the proposed method and its superior performance compared to other competing methods.

The challenging issues for the practical application of lithium metal batteries (LMBs) are the fragile solidelectrolyte interphase (SEI) layer and uncontrollable growth of Li dendrites induced by nonuniform Li deposition. In this work, to enable homogeneous Li deposition and a stable SEI layer, a fluorinated gold (Au) doped titanium silicalite (ATSF) is prepared by a facile synthetic process to modify the Cu current collector for a stable Li metal anode. Driven by the Li affinity of Au nanoparticles and microporous properties of titanium silicalite, uniform Li nucleation and homogeneous Li deposition can be realized on the modified Cu current collector. Simultaneously, the doping of fluorine in ATSF facilitates the formation of the LiF-rich SEI layer for a more stable Li stripping/plating process. Consequently, the ATSF5/Cu current collector exhibits a high coulombic efficiency (CE) of 96.8% even after 600 cycles and a long-term cycling stability of 1100 h with remarkably low and stable overpotential. When paired with a high-mass-loading LiFePO4 cathode (∼2.3 mA h cm−2), the Li-metal full cell demonstrates an exceptional capacity retention of 97.9% with a high CE of 99.8% after 200 cycles. Furthermore, the LFP full cell with the Li@ATSF-5/Cu anode also displays superior cycling performance under harsh test conditions. This simple current collector modification through constructing a multi-functional zeolite matrix layer provides a new choice for achieving stable cycling performance of LMBs. [ABSTRACT FROM AUTHOR]

Due to the high‐risk working environment of high‐voltage transmission lines, defect samples of strain clamps cannot be fully and completely collected. As a result, the deep learning method based on defect sample tags cannot effectively identify all abnormalities. To solve this problem, an unsupervised anomaly detection method based on knowledge distillation is proposed, which only requires a small number of normal samples to drive the model for anomaly detection. ResNet is the framework of the teacherstudent model, and the feature activation layer after ResBlock is used for knowledge transfer. Residual‐assisted attention and pyramid‐splitting attention were used to enhance the spatial perception and multi‐scale information utilization ability of the model. This model only transmits the information of normal samples and is sensitive to abnormal samples. The proposed model outperformed the baseline by 23% and individual categories by 78% on the MVTec AD (Anomaly Detection Dataset) and outperformed the baseline by 45% and individual categories by 10% on the CIFAR10 and is also reliable for Mnist and Fashion Mnist. This method performs best (82.71%) over the existing method on the self‐built data set. [ABSTRACT FROM AUTHOR]