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Machine learning (ML) can help to design layered double hydroxides (LDHs) with excellent properties as catalysts for the oxygen evolution reaction (OER).

The leaching and recycling of valuable metals via environmentally benign solvents is important because of the ever-increasing waste lithium-ion batteries, but it remains a challenge. Herein, a multi-functional deep eutectic solvent (DES) based on lactic acid (LA) and guanidine hydrochloride (GHC) was used to extract cobalt and lithium ions from LiCoO

State-of-the-art spoofing speech detection methods highly depend on hand-crafted features like LFCC, MFCC, CQCC, FFT spectrum, etc. But almost for all the features, during extracting procedure, some information will be lost permanently, which may be of vital importance to spoofing detection task. Most speeches on the web, are often accompanied by music or noise. Using monaural speech separation to get human voice and then doing spoofing detection later may be a natural idea, but it has two shortcomings: two models must be trained, and during first separation step, some voice will be filtered out while noise will be kept. In this paper, we propose an end-to-end anti-spoofing model which fully consists of one-dimensional convolutional neural networks for spoofing detection under noisy conditions. The proposed system achieved an EER of 1.50% on the ASVspoof2019 logical access evaluation set. Under noisy conditions, we proposed a novel approach based on knowledge distillation, and our proposed method achieved great performance improvements compared to multi-condition training method.

Coronatine (COR) is a non-host specific phytotoxin secreted by Pseudomonas syringae pv. tomato that can induce leaf chlorosis and increase the virulence of pathogens during plant-pathogen interactions. Studies have shown that COR can regulate multiple physiological processes in plants, but its involvement in bacterial pathogenesis and plant growth regulation is not well understood. In this study, transcriptome sequencing was carried out on 4-week-old tomato leaves that were either mock-treated or treated with COR. Transcriptome sequencing led to the identification of 6144 differentially expressed genes (DEGs), of which 4361 genes were downregulated and 1783 genes were upregulated upon COR treatment. To obtain functional information on the DEGs, we annotated these genes using GO and KEGG databases. Functional classification analysis showed that the DEGs were primarily involved in photosynthesis, chlorophyll and carotenoid biosynthesis, jasmonic acid (JA) synthesis and phenylpropane metabolism. A total of 23 genes related to chlorophyll biosynthesis had significant changes, of which 22 genes were downregulated and one gene was upregulated, indicating that chlorophyll biosynthesis was inhibited upon COR treatment. A total of 17 photosystem I related genes and 22 photosystem II related genes involving 20 protein subunits were also downregulated. In the JA synthesis pathway, 25 genes were up regulated, and six genes were downregulated in COR treated samples. COR was also involved in the regulation of multiple secondary metabolites. The identified DEGs will help us better understand the virulence effects and physiological functions of COR and provide a theoretical basis for breeding resistance into economically important crops.

Rat cyborg is a rat which can receive cerebral commands and act as human wants. The navigation task is a widely used scenario in which the rat cyborg is required to walk along a specified path according to commands. Mostly the commands are given by human operators, but there's a need for automatic control which is quite challenging since several modules need to be elaborately combined together to work as a whole, among which the monitoring system and the commands model are two major components. Previously, few attempts were made for behavior monitoring of rat cyborg. Existing works often implement an over-simple rat information extractor which is capable of giving only a few parameters of the rat cyborg, which greatly limited the performance of their automatic control accuracy. In response to this requirement, we develop a monitoring system that is capable of giving detailed motion parameters and accurate body postures of the rat cyborg. We explore the possibility of recognizing rat postures using shape information, which is described by the powerful Zernike moments. We propose several simple shape descriptors which are fast to compute and achieve acceptable performance.

A gram-negative bacterium GXGL-4A was originally isolated from maize roots. It displayed nitrogen-fixing (NF) ability under nitrogen-free culture condition, and had a significant promotion effect on cucumber growth in the pot inoculation test. The preliminary physiological and biochemical traits of GXGL-4A were characterized. Furthermore, a phylogenetic tree was constructed based on 16S ribosomal DNA (rDNA) sequences of genetically related species. To determine the taxonomic status of GXGL-4A and further utilize its nitrogen-fixing potential, genome sequence was obtained using PacBio RS II technology. The analyses of average nucleotide identity based on BLAST+ (ANIb) and correlation indexes of tetra-nucleotide signatures (Tetra) showed that the NF isolate GXGL-4A is closely related to the Kosakonia radicincitans type strain DSM 16656. Therefore, the isolate GXGL-4A was eventually classified into the species of Kosakonia radicincitans and designated K. radicincitans GXGL-4A. A high consistency in composition and gene arrangement of nitrogen-fixing gene cluster I (nif cluster I) was found between K. radicincitans GXGL-4A and other Kosakonia NF strains. The mutants tagged with green fluorescence protein (GFP) were obtained by transposon Tn5 mutagenesis, and then, the colonization of gfp-marked K. radicincitans GXGL-4A cells on cucumber seedling root were observed under fluorescence microscopy. The preferential sites of the labeled GXGL-4A cell population were the lateral root junctions, the differentiation zone, and the elongation zone. All these results should benefit for the deep exploration of nitrogen fixation mechanism of K. radicincitans GXGL-4A and will definitely facilitate the genetic modification process of this NF bacterium in sustainable agriculture.

Growing demands for automated analysis of animal behavior in areas such as neuroscience, psychology, genetics and pharmacology have been witnessed in recent decades. Some progresses have been made, but studies on social behavior analysis, which is more challenging, are rarely seen and almost all of them rely on hand-crafted features. Motivated by the concept of word embedding in NLP and the success of deep learning, we present a method that extracts features for both of the mouse agents involved in social behavior events and the scenario context using embedding networks, then uses an LSTM network to model the behaviors based on the agent and context embeddings. Our method is tested on a novel dataset, RatSI [8]. We find our mouse state embedding method outperforms traditional hand-crafted feature based methods.

This paper presents the first report of a system of human’s speech interaction with rats via integration of brain–machine interfaces and automatic speech recognition technologies. We propose a novel human–rat speech interaction paradigm by incorporating speech translator module, which translates human’s speech commands into suitable electrical brain stimulation to steer the rat to induce expected locomotor behaviors. The preliminary results show that we can guide a rat’s movement by speech commands. We further look into the future application scenarios together with forthcoming challenges facing this newly evolved cyborg intelligent system. This work will pave the way for natural interaction with animal robots.

With wide usage and special needs of AV transmission services on the Internet, streaming media transport protocol and its technology is studied. In order to effectively resolve and control highly sensitive delay problem on the streaming media services and on carefully comparing TCP and UDP features, an UDP-based streaming media transmission multicast system is designed and implemented. By experiment, a delay time performance in different playout buffer (PB) length and different client numbers are achieved. The results demonstrated the real-time advantage of UDP on streaming media transmission.

MIMO technology can be divided into two ways: based on the spatial multiplexing technique, such as VBLAST, and based on the launch diversity technology, such as STBC. This paper proposes a structure which mixes the two ways, that is STBC-VBLAST structure. In principle of minimizing pair-error probability, we design the precoding matrix of this system using the features beam molding algorithm. This proposal is suitable for TDD mode. It takes advantage of CSI to reduce error rate and increase capacity. Simulation results show that this algorithm could achieve remarkable effects, especially in low constellation dimension.

This chapter primarily studies the MAP and the SOVA, and discusses the suitability of combining both algorithms. An iterative decoding algorithm is improved. The frames are first applied with SOVA iterations with error detection for iteration termination. After a fixed maximum number of SOVA iterations, the erroneous frames are continued with Log-MAP iterations. The extrinsic soft output from the last SOVA iteration are selectively fed to the first Log-MAP iteration as initial priori input. The result of the simulation shows that the improved algorithm could reduce the complexity and keep high performance.

A new precoding technique is proposed for STBC-VBLAST hybrid scheme in this paper. In principle of minimizing pair-error probability, we design the precoding matrix of this system using the features beam molding algorithm. This proposal takes advantage of CSI to reduce error rate and increase capacity. Simulation results show that this algorithm could achieve remarkable effects, especially in low constellation dimension.

This paper aims at improving the re-implementation of existing information systems when they are called to be involved in a system of systems, i.e. a federation of enterprise information systems that interoperate. The idea is reusing the local experiences coming from the development of the original information system with the process of Model Discovery and Ontological approach. We draw the strong points of MDA and HLA, former in transforming concepts and models from the conceptual level to the implementation and latter in implementation of distributed systems, then we propose a MDA/HLA framework to implement distributed enterprise models from the conceptual level of federated enterprise interoperability approach. In addition, we propose a model reversal methodology to help re-implement the legacy information system, in order to achieve the interoperability with other systems. We also draw the strong points from web services in order to improve the performance of HLA in distribute simulation.