Induced defense strategies of plants against Ralstonia solanacearum

Plants respond to Ralstonia solanacearum infestation through two layers of immune system (PTI and ETI). This process involves the production of plant-induced resistance. Strategies for inducing resistance in plants include the formation of tyloses, gels, and callose and changes in the content of cell wall components such as cellulose, hemicellulose, pectin, lignin, and suberin in response to pathogen infestation. When R. solanacearum secrete cell wall degrading enzymes, plants also sense the status of cell wall fragments through the cell wall integrity (CWI) system, which activates deep-seated defense responses. In addition, plants also fight against R. solanacearum infestation by regulating the distribution of metabolic networks to increase the production of resistant metabolites and reduce the production of metabolites that are easily exploited by R. solanacearum. We review the strategies used by plants to induce resistance in response to R. solanacearum infestation. In particular, we highlight the importance of plant-induced physical and chemical defenses as well as cell wall defenses in the fight against R. solanacearum

Inferring Economic Condition Uncertainty from Electricity Big Data

Inferring the uncertainties in economic conditions are of significant importance for both decision makers as well as market players. In this paper, we propose a novel method based on Hidden Markov Model (HMM) to construct the Economic Condition Uncertainty (ECU) index that can be used to infer the economic condition uncertainties. The ECU index is a dimensionless index ranges between zero and one, this makes it to be comparable among sectors, regions and periods. We use the daily electricity consumption data of nearly 20 thousand firms in Shanghai from 2018 to 2020 to construct the ECU indexes. Results show that all ECU indexes, no matter at sectoral level or regional level, successfully captured the negative impacts of COVID-19 on Shanghai's economic conditions. Besides, the ECU indexes also presented the heterogeneities in different districts as well as in different sectors. This reflects the facts that changes in uncertainties of economic conditions are mainly related to regional economic structures and targeted regulation policies faced by sectors. The ECU index can also be easily extended to measure uncertainties of economic conditions in different fields which has great potentials in the future

A Bi2Te3-Filled Nickel Foam Film with Exceptional Flexibility and Thermoelectric Performance

The past decades have witnessed surging demand for wearable electronics, for which thermoelectrics (TEs) are considered a promising self-charging technology, as they are capable of converting skin heat into electricity directly. Bi2Te3 is the most-used TE material at room temperature, due to a high zT of ~1. However, it is different to integrate Bi2Te3 for wearable TEs owing to its intrinsic rigidity. Bi2Te3 could be flexible when made thin enough, but this implies a small electrical and thermal load, thus severely restricting the power output. Herein, we developed a Bi2Te3/nickel foam (NiFoam) composite film through solvothermal deposition of Bi2Te3 nanoplates into porous NiFoam. Due to the mesh structure and ductility of Ni Foam, the film, with a thickness of 160 μm, exhibited a high figure of merit for flexibility, 0.016, connoting higher output. Moreover, the film also revealed a high tensile strength of 12.7 ± 0.04 MPa and a maximum elongation rate of 28.8%. In addition, due to the film’s high electrical conductivity and enhanced Seebeck coefficient, an outstanding power factor of 850 μW m−1 K−2 was achieved, which is among the highest ever reported. A module fabricated with five such n-type legs integrated electrically in series and thermally in parallel showed an output power of 22.8 nW at a temperature gap of 30 K. This work offered a cost-effective avenue for making highly flexible TE films for power supply of wearable electronics by intercalating TE nanoplates into porous and meshed-structure materials

Potential of Core-Collapse Supernova Neutrino Detection at JUNO

JUNO is an underground neutrino observatory under construction in Jiangmen, China. It uses 20kton liquid scintillator as target, which enables it to detect supernova burst neutrinos of a large statistics for the next galactic core-collapse supernova (CCSN) and also pre-supernova neutrinos from the nearby CCSN progenitors. All flavors of supernova burst neutrinos can be detected by JUNO via several interaction channels, including inverse beta decay, elastic scattering on electron and proton, interactions on C12 nuclei, etc. This retains the possibility for JUNO to reconstruct the energy spectra of supernova burst neutrinos of all flavors. The real time monitoring systems based on FPGA and DAQ are under development in JUNO, which allow prompt alert and trigger-less data acquisition of CCSN events. The alert performances of both monitoring systems have been thoroughly studied using simulations. Moreover, once a CCSN is tagged, the system can give fast characterizations, such as directionality and light curve

Detection of the Diffuse Supernova Neutrino Background with JUNO

As an underground multi-purpose neutrino detector with 20 kton liquid scintillator, Jiangmen Underground Neutrino Observatory (JUNO) is competitive with and complementary to the water-Cherenkov detectors on the search for the diffuse supernova neutrino background (DSNB). Typical supernova models predict 2-4 events per year within the optimal observation window in the JUNO detector. The dominant background is from the neutral-current (NC) interaction of atmospheric neutrinos with 12C nuclei, which surpasses the DSNB by more than one order of magnitude. We evaluated the systematic uncertainty of NC background from the spread of a variety of data-driven models and further developed a method to determine NC background within 15\% with {\it{in}} {\it{situ}} measurements after ten years of running. Besides, the NC-like backgrounds can be effectively suppressed by the intrinsic pulse-shape discrimination (PSD) capabilities of liquid scintillators. In this talk, I will present in detail the improvements on NC background uncertainty evaluation, PSD discriminator development, and finally, the potential of DSNB sensitivity in JUNO

Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

Core-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure

A Structured Recognition Method for Invoices Based on StrucTexT Model

Invoice recognition has long been an active research direction in the field of image recognition. Existing invoice recognition methods suffer from a low recognition rate for structured invoices, a slow recognition speed, and difficulty in mobile deployment. To address these issues, we propose an invoice-structured recognition method based on the StrucTexT model. This method uses the idea of knowledge distillation to speed up the recognition speed and compress the model size without reducing the model recognition rate; this is achieved using the teacher model StrucTexT to guide the student model StrucTexT_slim. The method can effectively solve the problems of slow model recognition speed and large model size that make mobile deployment difficult with traditional methods. Experimental results show that the proposed model achieves an accuracy rate of over 94% on the SROIE and FUNSD public datasets and over 95% on the self-built structured invoice dataset. In addition, the method is 30% faster than other models (YOLOv4, LeNet-5, and Tesseract-OCR) in terms of recognition speed, while the model size is compressed by about 20%