Deep Reinforcement Learning for Flipper Control of Tracked Robots

The autonomous control of flippers plays an important role in enhancing the intelligent operation of tracked robots within complex environments. While existing methods mainly rely on hand-crafted control models, in this paper, we introduce a novel approach that leverages deep reinforcement learning (DRL) techniques for autonomous flipper control in complex terrains. Specifically, we propose a new DRL network named AT-D3QN, which ensures safe and smooth flipper control for tracked robots. It comprises two modules, a feature extraction and fusion module for extracting and integrating robot and environment state features, and a deep Q-Learning control generation module for incorporating expert knowledge to obtain a smooth and efficient control strategy. To train the network, a novel reward function is proposed, considering both learning efficiency and passing smoothness. A simulation environment is constructed using the Pymunk physics engine for training. We then directly apply the trained model to a more realistic Gazebo simulation for quantitative analysis. The consistently high performance of the proposed approach validates its superiority over manual teleoperation

Federated Reinforcement Learning for Electric Vehicles Charging Control on Distribution Networks

With the growing popularity of electric vehicles (EVs), maintaining power grid stability has become a significant challenge. To address this issue, EV charging control strategies have been developed to manage the switch between vehicle-to-grid (V2G) and grid-to-vehicle (G2V) modes for EVs. In this context, multi-agent deep reinforcement learning (MADRL) has proven its effectiveness in EV charging control. However, existing MADRL-based approaches fail to consider the natural power flow of EV charging/discharging in the distribution network and ignore driver privacy. To deal with these problems, this paper proposes a novel approach that combines multi-EV charging/discharging with a radial distribution network (RDN) operating under optimal power flow (OPF) to distribute power flow in real time. A mathematical model is developed to describe the RDN load. The EV charging control problem is formulated as a Markov Decision Process (MDP) to find an optimal charging control strategy that balances V2G profits, RDN load, and driver anxiety. To effectively learn the optimal EV charging control strategy, a federated deep reinforcement learning algorithm named FedSAC is further proposed. Comprehensive simulation results demonstrate the effectiveness and superiority of our proposed algorithm in terms of the diversity of the charging control strategy, the power fluctuations on RDN, the convergence efficiency, and the generalization ability

On-site and visual detection of sorghum mosaic virus and rice stripe mosaic virus based on reverse transcription-recombinase-aided amplification and CRISPR/Cas12a

Rapid, sensitive and visual detection of plant viruses is conducive to effective prevention and control of plant viral diseases. Therefore, combined with reverse transcription and recombinase-aided amplification, we developed a CRISPR/Cas12a-based visual nucleic acid detection system targeting sorghum mosaic virus and rice stripe mosaic virus, which cause harm to crop production in field. When the RT-RAA products were recognized by crRNA and formed a complex with LbCas12a, the ssDNA labeled with a quenched green fluorescent molecule will be cleaved by LbCas12a, and then a significant green fluorescence signal will appear. The entire detection process can be completed within 30 min without using any sophisticated equipment and instruments. The detection system could detect samples at a dilution of 107, about 104-fold improvement over RT-PCR, so the system was successfully to detect rice stripe mosaic virus in a single leafhopper, which is the transmission vector of the virus. Finally, the CRISPR/Cas12a-based detection system was utilized to on-site detect the two viruses in the field, and the results were fully consistent with that we obtained by RT-PCR in laboratory, demonstrating that it has the application prospect of detecting important crop viruses in the field

Molecular characterization of SARS-CoV-2 nucleocapsid protein

Corona Virus Disease 2019 (COVID-19) is a highly prevalent and potent infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Until now, the world is still endeavoring to develop new ways to diagnose and treat COVID-19. At present, the clinical prevention and treatment of COVID-19 mainly targets the spike protein on the surface of SRAS-CoV-2. However, with the continuous emergence of SARS-CoV-2 Variants of concern (VOC), targeting the spike protein therapy shows a high degree of limitation. The Nucleocapsid Protein (N protein) of SARS-CoV-2 is highly conserved in virus evolution and is involved in the key process of viral infection and assembly. It is the most expressed viral structural protein after SARS-CoV-2 infection in humans and has high immunogenicity. Therefore, N protein as the key factor of virus infection and replication in basic research and clinical application has great potential research value. This article reviews the research progress on the structure and biological function of SARS-CoV-2 N protein, the diagnosis and drug research of targeting N protein, in order to promote researchers’ further understanding of SARS-CoV-2 N protein, and lay a theoretical foundation for the possible outbreak of new and sudden coronavirus infectious diseases in the future

Ubr3, a Novel Modulator of Hh Signaling Affects the Degradation of Costal-2 and Kif7 Through Poly-Ubiquitination

Hedgehog (Hh) signaling regulates multiple aspects of metazoan development and tissue homeostasis, and is constitutively active in numerous cancers. We identified Ubr3, an E3 ubiquitin ligase, as a novel, positive regulator of Hh signaling in Drosophila and vertebrates. Hh signaling regulates the Ubr3-mediated poly-ubiquitination and degradation of Cos2, a central component of Hh signaling. In developing Drosophila eye discs, loss of ubr3 leads to a delayed differentiation of photoreceptors and a reduction in Hh signaling. In zebrafish, loss of Ubr3 causes a decrease in Shh signaling in the developing eyes, somites, and sensory neurons. However, not all tissues that require Hh signaling are affected in zebrafish. Mouse UBR3 poly-ubiquitinates Kif7, the mammalian homologue of Cos2. Finally, loss of UBR3 up-regulates Kif7 protein levels and decreases Hh signaling in cultured cells. In summary, our work identifies Ubr3 as a novel, evolutionarily conserved modulator of Hh signaling that boosts Hh in some tissues

Mapping and validation of sex-linked SNP markers in the swimming crab Portunus trituberculatus

Portunus trituberculatus is one of the most commercially important marine crustacean species for both aquaculture and fisheries in Southeast and East Asia. Production of monosex female stocks is attractive in commercial production since females are more profitable than their male counterparts. Identification and mapping of the sex-linked locus is an efficient way to elucidate the mechanisms of sex determination in the species and support the development of protocols for monosex female production. In this study, a sex-averaged map and two sex-specific genetic maps were constructed based on 2b-restriction site-associated DNA sequencing. A total of 6349 genetic markers were assigned to 53 linkage groups. Little difference was observed in the pattern of sex-specific recombination between females and males. Association analysis and linkage mapping identified 7 markers strongly associated with sex, four of which were successfully mapped on the extremity of linkage group 22. Females were homozygous and males were heterozygous for those 7 markers strongly suggesting an XX/XY sex determination system in this species. Three Markers were successfully validated in a wild population of P. trituberculatus and exhibited a specificity ranging from 93.3% to 100%. A high-resolution melting based assay was developed for sex genotyping. This study provides new knowledge and tools for sex identification which will help the development of protocols for monosex female production of P. trituberculatus and support future genomic studies

High-performance infrared photodetectors based on InAs/InAsSb/AlAsSb superlattice for 3.5 µm cutoff wavelength spectra

High-performance infrared p-i-n photodetectors based on InAs/InAsSb/AlAsSb superlattices on GaSb substrate have been demonstrated at 300K. These photodetectors exhibit 50% and 100% cut-off wavelength of ∼3.2 µm and ∼3.5 µm, respectively. Under -130 mV bias voltage, the device exhibits a peak responsivity of 0.56 A/W, corresponding to a quantum efficiency (QE) of 28%. The dark current density at 0 mV and -130 mV bias voltage are 8.17 × 10−2 A/cm2 and 5.02 × 10−1 A/cm2, respectively. The device exhibits a saturated dark current shot noise limited specific detectivity (D*) of 3.43 × 109 cm·Hz1/2/W (at a peak responsivity of 2.5 µm) under -130 mV of applied bias