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Abstract The use of pulse voltage can greatly improve the precision of electrochemical microfabrication, and the narrower the pulse width of the applied pulse voltage signal, the higher the machining precision. However, the commonly used chopper circuit topology of pulse power supplies is limited by the maximum switching frequency of the field-effect transistor. To address this problem, this paper proposes a nanosecond pulse electrochemical micromachining power supply based on a differential circuit. The power supply uses the STM32F103C8T6 microcontroller as the control core to output high-performance rectangular waves through a DDS device. After differential, rectification, filtering, and power amplification processing, stable, frequency, amplitude, and pulse width adjustable spike pulse voltage signals are obtained. By establishing a system mathematical model and optimizing the time constant of the differential circuit, theoretically, the sub-nanosecond pulse width can be obtained. Prototype performance tests show that the power supply has a maximum frequency of 20 MHz, a minimum pulse width of 1.8 ns, and a maximum peak voltage of 10 V. By using this power supply for microhole electrochemical machining experiments, nanometer-level machining precision has been achieved.

Abstract High‐dimensional imbalanced multi‐classification problems (HDIMCPs) occur frequently in engineering applications such as medical detection, item classification, and email classification. However, there is a paucity of research in the academic community on this topic. This paper proposes an evolutionary algorithm‐based classification method for HDIMCPs, named HIMALO (high‐dimensional imbalanced multi‐classification method based on ant lion optimizer). HIMALO proposes a new individual initialization strategy that replaces the random initialization of the ant lion optimizer with Fuch chaos. Then, it encodes individuals using concatenated sample features and base classifier weights, optimizes these features and weights concurrently during the iteration process. Additionally, a multi‐classification strategy, union one versus many, that combines one versus all and one‐against‐higher‐order is proposed. Numerous experiments are conducted to prove the superior classification performance and stability of HIMALO when compared with other algorithms.

Abstract Feature selection is a widely used data pre‐processing method. However, the research on feature selection stability is very rare. Although there are some related studies that mainly focus on filters rather than wrappers, especially wrappers based on evolutionary algorithms. In this paper, a stable feature selection method called stable brain storm optimisation (SBSO) is proposed. It puts forward a new population initialisation strategy, which treats stable feature selection results as guide information for initialisation and utilises such chaos to initialise population. SBSO also sets up an information archive to store the historical optimal individuals dynamically. A large number of experiments show that SBSO gives excellent classification performance when compared with other methods, and superior feature selection stability when compared with other wrappers.

Wireless communications and networking are playing an important role in coordination and cooperation of multi-robot systems (MRS). However, it is challenging to keep a reliable and stable wireless connection in practical applications. Especially, robots acting in electromagnetic adversarial (EA) environments may encounter more serious situations including scarce spectrum, active interference, adversarial competition, etc. In this survey, we firstly analyze the challenges faced by MRS in EA environments, and provide a categorization according to the “sense-decide-act” robot control procedure. Secondly, enabling techniques for each challenge are introduced. Finally, typical robotics software architectures are introduced, as frameworks for efficient arrangement of the above mentioned enabling techniques.

Intelligent unmanned aerial vehicles (UAVs) have been applied for civil and military uses. Relative localization (RL) is crucial for multi-UAVs to accomplish complex tasks successfully and safely. In global positioning system (GPS) denied environments, where accurate or meaningful location information is hard to obtain, persistent excitation based RL is a promising approach for multi-UAVs to achieve RL without any needs of external infrastructures. However, for many cases, existing persistent excitation based RL method suffers precision loss, error accumulation and divergence. This article tackles these issues, and proposes an enhanced approach to ensure the practical usage of persistent excitation based RL. Synchronized sensor sample prediction is introduced to confine and reduce RL error, and RL estimation is redesigned to avoid RL error accumulation. We evaluated our solution by simulating various scenarios. The results show that the proposed approach can effectively decrease RL error and prevent RL error accumulation.

Summary: Background: Since late 2019, SARS-CoV-2 infection has resulted in COVID-19 accompanied by diverse clinical manifestations. However, the underlying mechanism of how SARS-CoV-2 interacts with host and develops multiple symptoms is largely unexplored. Methods: Bioinformatics analysis determined the sequence similarity between SARS-CoV-2 and human genomes. Diverse fragments of SARS-CoV-2 genome containing Human Identical Sequences (HIS) were cloned into the lentiviral vector. HEK293T, MRC5 and HUVEC were infected with laboratory-packaged lentivirus or transfected with plasmids or antagomirs for HIS. Quantitative RT-PCR and chromatin immunoprecipitation assay detected gene expression and H3K27ac enrichment, respectively. UV-Vis spectroscopy assessed the interaction between HIS and their target locus. Enzyme-linked immunosorbent assay evaluated the hyaluronan (HA) levels of culture supernatant and plasma of COVID-19 patients. Findings: Five short sequences (24–27 nt length) sharing identity between SARS-CoV-2 and human genome were identified. These RNA elements were highly conserved in primates. The genomic fragments containing HIS were predicted to form hairpin structures in silico similar to miRNA precursors. HIS may function through direct genomic interaction leading to activation of host enhancers, and upregulation of adjacent and distant genes, including cytokine genes and hyaluronan synthase 2 (HAS2). HIS antagomirs and Cas13d-mediated HIS degradation reduced HAS2 expression. Severe COVID-19 patients displayed decreased lymphocytes and elevated D-dimer, and C-reactive proteins, as well as increased plasma hyaluronan. Hymecromone inhibited hyaluronan production in vitro, and thus could be further investigated as a therapeutic option for preventing severe outcome in COVID-19 patients. Interpretation: HIS of SARS-CoV-2 could promote COVID-19 progression by upregulating hyaluronan, providing novel targets for treatment. Funding: The National Key R&D Program of China (2018YFC1005004), Major Special Projects of Basic Research of Shanghai Science and Technology Commission (18JC1411101), and the National Natural Science Foundation of China (31872814, 32000505).

Imbalanced data are very common in the real world, and it may deteriorate the performance of the conventional classification algorithms. In order to resolve the imbalanced classification problems, we propose an ensemble classification method that combines evolutionary under-sampling and feature selection. We employ the Bootstrap method in original data to generate many sample subsets. V-statistic is developed to measure the distribution of imbalanced data, and it is also taken as the optimization objective of the genetic algorithm for the under-sampling sample subsets. Moreover, we take F1 and Gmean indicators as two optimization objectives and employ the multiobjective ant colony optimization algorithm for feature selection of resampled data to construct an ensemble system. Ten low-dimensional and four high-dimensional typical imbalanced datasets are used in experiments. The six state-of-the-art algorithms and four measures are taken for a fair comparison. The experimental results show that our proposed system has a better classification performance compared with other algorithms, especially for the high-dimensional imbalanced data.

In this work, we deal with high-order solver for incompressible flow based on velocity correction scheme with discontinuous Galerkin discretized velocity and standard continuous approximated pressure. Recently, small time step instabilities have been reported for pure discontinuous Galerkin method, in which both velocity and pressure are discretized by discontinuous Galerkin. It is interesting to examine these instabilities in the context of mixed discontinuous Galerkin–continuous Galerkin method. By means of numerical investigation, we find that the discontinuous Galerkin–continuous Galerkin method shows great stability at the same configuration. The consistent velocity divergence discretization scheme helps to achieve more accurate results at small time step size. Since the equal order discontinuous Galerkin–continuous Galerkin method does not satisfy inf-sup stability requirement, the instability for high Reynolds number flow is investigated. We numerically demonstrate that fine mesh resolution and high polynomial order are required to obtain a robust system. With these conclusions, discontinuous Galerkin–continuous Galerkin method is able to achieve high-order spatial convergence rate and accurately simulate high Reynolds flow. The solver is tested through a series of classical benchmark problems, and efficiency improvement is proved against pure discontinuous Galerkin scheme.

Mesh partitioning is significant to the efficiency of parallel computational fluid dynamics simulations. The most time-consuming parts of parallel computational fluid dynamics simulations are iteratively solving linear systems derived from partial differential equation discretizations. This article aims at mesh partitioning for better iterative convergence feature of this procedure. For typical computational fluid dynamics simulations in which partial differential equations are discretized and solved after the mesh is partitioned, numerical information of the linear systems is not available yet during mesh partitioning. We propose to construct approximations for matrix elements and theoretically find out that for finite-volume-based problems, the face area can approximate the corresponding matrix element well. A mesh partitioning scheme using the matrix value approximations for better iterative convergence behavior is implemented and numerically testified. The results show that our method can capture the most important factor influencing the matrix values and achieve partitions with good performance throughout the simulations with non-uniform meshes. The novel partitioning strategy is general and easy to implement in various partitioning packages.

Attenuated strains of invasive enteric bacteria, such as Salmonella, represent promising gene delivery agents for nucleic acid-based vaccines as they can be administrated orally. In this study, we constructed a novel attenuated strain of Salmonella for the delivery and expression of the hemagglutinin (HA) and neuraminidase (NA) of a highly pathogenic H5N1 influenza virus. We showed that the constructed Salmonella strain exhibited efficient gene transfer activity for HA and NA expression and little cytotoxicity and pathogenicity in mice. Using BALB/c mice as the model, we evaluated the immune responses and protection induced by the constructed Salmonella-based vaccine. Our study showed that the Salmonella-based vaccine induced significant production of anti-HA serum IgG and mucosal IgA, and of anti-HA interferon-γ producing T cells in orally vaccinated mice. Furthermore, mice orally vaccinated with the Salmonella vaccine expressing viral HA and NA proteins were completely protected from lethal challenge of highly pathogenic H5N1 as well as H1N1 influenza viruses while none of the animals treated with the Salmonella vaccine carrying the empty expression vector with no viral antigen expression was protected. These results suggest that the Salmonella-based vaccine elicits strong antigen-specific humoral and cellular immune responses and provides effective immune protection against multiple strains of influenza viruses. Furthermore, our study demonstrates the feasibility of developing novel attenuated Salmonella strains as new oral vaccine vectors against influenza viruses.