Meta-Auto-Decoder: A Meta-Learning Based Reduced Order Model for Solving Parametric Partial Differential Equations

Many important problems in science and engineering require solving the so-called parametric partial differential equations (PDEs), i.e., PDEs with different physical parameters, boundary conditions, shapes of computational domains, etc. Typical reduced order modeling techniques accelarate solution of the parametric PDEs by projecting them onto a linear trial manifold constructed in the offline stage. These methods often need a predefined mesh as well as a series of precomputed solution snapshots, andmay struggle to balance between efficiency and accuracy due to the limitation of the linear ansatz. Utilizing the nonlinear representation of neural networks, we propose Meta-Auto-Decoder (MAD) to construct a nonlinear trial manifold, whose best possible performance is measured theoretically by the decoder width. Based on the meta-learning concept, the trial manifold can be learned in a mesh-free and unsupervised way during the pre-training stage. Fast adaptation to new (possibly heterogeneous) PDE parameters is enabled by searching on this trial manifold, and optionally fine-tuning the trial manifold at the same time. Extensive numerical experiments show that the MAD method exhibits faster convergence speed without losing accuracy than other deep learning-based methods

AI Widens the Gap between the Rich and the Poor

Since entering the 21st century, high technology has developed at a rapid pace. The development of high technology has changed the methods of production and lifestyle of human beings. While enjoying the efficiency, comfort, and convenience brought by high technologies, people find that the gap between the rich and the poor has been widening. More and more attentions have been paid to the influences on the gap between the rich and the poor arising from the development of high technologies, especially from the development of artificial intelligence (AI) technology. This paper focuses on this social phenomenon and demonstrates that the development of AI will widen the gap between the rich and the poor. The paper will proceed with the discussion from three levels of human actives: individual, company, and country

Optimization Calculation Method of Injection Voltage and Series Converter Capacity for Unified Power Flow Controller

[Introduction] The application of UPFC (Unified Power Flow Controller) is to increase the transmission capacity of key sections, which mainly through the injection of a certain series voltage into the system and therefore divert the power flow of target line. [Method] Focusing on the key transmission sections, the regional power grid was divided into the transmission network, the interconnection network and the internal network based on their function, then the Gaussian elimination method was used to equalize the system network into a two-channel constant power exchange system. On this basis, based on the energy conservation principle, the phasor method was applied to provide phasor graph of the voltage, power angle, impedance, and active power relationship between the UPFC branch and the equivalent branch before and after the UPFC was put into operation, respectively, for the stand-alone infinity system and the constant power exchange system. The classical power transfer function was used to deduced the UPFC injection voltage and series converter capacity calculation formula. [Result] The calculation method is simple and practical, especially suitable for power system planning and design stage. [Conclusion] The above method is applied to the calculation of Shenzhen power grid example, and compared with the simulation results of PSCAD, the validity and practicability of the method are verified

Selection of Reference Genes for RT-qPCR Analysis in a Predatory Biological Control Agent, \u3cem\u3eColeomegilla maculata\u3c/em\u3e (Coleoptera: Coccinellidae)

Reverse transcriptase-quantitative polymerase chain reaction (RT-qPCR) is a reliable technique for quantifying gene expression across various biological processes, of which requires a set of suited reference genes to normalize the expression data. Coleomegilla maculata (Coleoptera: Coccinellidae), is one of the most extensively used biological control agents in the field to manage arthropod pest species. In this study, expression profiles of 16 housekeeping genes selected from C. maculata were cloned and investigated. The performance of these candidates as endogenous controls under specific experimental conditions was evaluated by dedicated algorithms, including geNorm, Normfinder, BestKeeper, and ΔCt method. In addition, RefFinder, a comprehensive platform integrating all the above-mentioned algorithms, ranked the overall stability of these candidate genes. As a result, various sets of suitable reference genes were recommended specifically for experiments involving different tissues, developmental stages, sex, and C. maculate larvae treated with dietary double stranded RNA. This study represents the critical first step to establish a standardized RT-qPCR protocol for the functional genomics research in a ladybeetle C. maculate. Furthermore, it lays the foundation for conducting ecological risk assessment of RNAi-based gene silencing biotechnologies on non-target organisms; in this case, a key predatory biological control agent

Stable Reference Gene Selection for RT-qPCR Analysis in Nonviruliferous and Viruliferous \u3cem\u3eFrankliniella occidentalis\u3c/em\u3e

Reverse transcriptase-quantitative polymerase chain reaction (RT-qPCR) is a reliable technique for measuring and evaluating gene expression during variable biological processes. To facilitate gene expression studies, normalization of genes of interest relative to stable reference genes is crucial. The western flower thrips Frankliniella occidentalis (Pergande) (Thysanoptera: Thripidae), the main vector of tomato spotted wilt virus (TSWV), is a destructive invasive species. In this study, the expression profiles of 11 candidate reference genes from nonviruliferous and viruliferous F. occidentalis were investigated. Five distinct algorithms, geNorm, NormFinder, BestKeeper, the ΔCt method, and RefFinder, were used to determine the performance of these genes. geNorm, NormFinder, BestKeeper, and RefFinder identified heat shock protein 70 (HSP70), heat shock protein 60 (HSP60), elongation factor 1 α, and ribosomal protein l32 (RPL32) as the most stable reference genes, and the ΔCt method identified HSP60, HSP70, RPL32, and heat shock protein 90 as the most stable reference genes. Additionally, two reference genes were sufficient for reliable normalization in nonviruliferous and viruliferous F. occidentalis. This work provides a foundation for investigating the molecular mechanisms of TSWV and F. occidentalis interactions

Canopy Spectral Characterization of Wheat Stripe Rust in Latent Period

Stripe rust, caused by Puccinia striiformis f. sp. tritici (Pst), is one of the important wheat diseases worldwide. In this study, the spectral data were collected from wheat canopy during the latent period inoculated with three different concentrations of urediniospores and classification models based on discriminant partial least squares (DPLS) were built to differentiate leaves with and without infection of the stripe rust pathogen. The effects of different spectra features, wavebands, and the number of the samples used in modeling on the performances of the models were assessed. The results showed that, in the spectral region of 325-1075 nm, the model with the spectral feature of 2nd derivative of Pseudoabsorption index had better accuracy than others. The average accuracy rate was 97.28% for the training set and 92.98% for the testing set. In the waveband of 925-1075 nm, the model with the spectral feature of 1st derivative Pseudoabsorption index had better accuracy than other models, and the average accuracy rates were 98.27% and 94.33% for the training and testing sets, respectively. The results demonstrated that wheat stripe rust in latent period can be qualitatively identified based on the canopy spectral detection. Thus, the method can be used for early monitoring of infections of wheat stripe rust

Modified Quasi-Steady State Model of DC System for Transient Stability Simulation under Asymmetric Faults

As using the classical quasi-steady state (QSS) model could not be able to accurately simulate the dynamic characteristics of DC transmission and its controlling systems in electromechanical transient stability simulation, when asymmetric fault occurs in AC system, a modified quasi-steady state model (MQSS) is proposed. The model firstly analyzes the calculation error induced by classical QSS model under asymmetric commutation voltage, which is mainly caused by the commutation voltage zero offset thus making inaccurate calculation of the average DC voltage and the inverter extinction advance angle. The new MQSS model calculates the average DC voltage according to the actual half-cycle voltage waveform on the DC terminal after fault occurrence, and the extinction advance angle is also derived accordingly, so as to avoid the negative effect of the asymmetric commutation voltage. Simulation experiments show that the new MQSS model proposed in this paper has higher simulation precision than the classical QSS model when asymmetric fault occurs in the AC system, by comparing both of them with the results of detailed electromagnetic transient (EMT) model of the DC transmission and its controlling system