Research on Total Electric Field Prediction Method of Ultra-High Voltage Direct Current Transmission Line Based on Stacking Algorithm

Ultra-high voltage(UHV)transmission lines are an important part of China’s power grid and are often surrounded by a complex electromagnetic environment.The ground total electric field is considered a main electromagnetic environment indicator of UHV transmission lines and is currently employed for reliable long-term operation of the power grid.Yet,the accurate prediction of the ground total electric field remains a technical challenge.In this work,we collected the total electric field data from the Ningdong-Zhejiang±800 kV UHVDC transmission project,as of the Ling Shao line,and perform an outlier analysis of the total electric field data.We show that the Local Outlier Factor(LOF)elimination algorithm has a small average difference and overcomes the performance of Density-Based Spatial Clustering of Applications with Noise(DBSCAN)and Isolated Forest elimination algorithms.Moreover,the Stacking algorithm has been found to have superior prediction accuracy than a variety of similar prediction algorithms,including the traditional finite element.The low prediction error of the Stacking algorithm highlights the superior ability to accurately forecast the ground total electric field of UHVDC transmission lines.

Identification and characterization of a curly-leaf locus CL1 encoding an IAA2 protein in Brassica napus

The leaf is the main organ for rapeseed photosynthesis,and its morphology influences photosynthetic efficiency and supports increased planting density and yield.However,the molecular regulatory mechanism of leaf morphology in Brassica napus is poorly understood,restricting progress in breeding for the trait.We describe a novel dominant mutation,curly leaf 1(cl1),which confers uneven dorsal–ventral axis development,irregular cellular structure and influenced gravitropic response in the seedling stage.The CL1 locus was mapped to a 1.573-Mb interval on chromosome A05 using simple sequence repeat(SSR)markers,and co-segregated with the phenotype of plants in the curly F2 population.A substitution(P62S)was identified in the highly conserved degron motif(GWSPV)of the IAA2 protein in the cl1mutant,and the P62S substitution impaired the interaction between IAA2 and TIR1 in the presence of auxin,influencing auxin signaling.The P62S substitution-induced curly leaf phenotype was verified by ectopic expression of Bna A05.iaa2 in Arabidopsis and B.napus.Our findings explain the function of IAA2 in rapeseed,providing a foundation for future investigation of auxin signaling and the mechanisms underlying leaf development in B.napus.

Prediction and Analysis on Short-term Load of Power System Based on LSTM

Effective short-term prediction of regional voltage load is of great significance to the implementation of energy saving and emission reduction policies in China.Accurate prediction of real-time demand voltage can reduce power waste and carbon emissions,make outstanding contributions to delaying global climate warming,and is conducive to global environmental protection and sustainable development.On the short-term load forecasting of power system,a variant model of RNN-LSTM is tested in this paper.It effectively solves the problem of gradient explosion and disappearance caused by large amount of data input in classical RNN.On the basis of this model,optimization experiments are carried out under different super parameters to achieve better prediction results.The experimental results show that the accuracy of test set reaches 99.8%,which proves that the method proposed in this paper has certain reference value.

Time Prediction Through A Congested Road Section

First,the cellular automaton was used to simulate a"T"junction,and the correlation analysis was performed by combining the traffic pattern and the corresponding data to obtain the reason for the inaccurate prediction time of the navigation software.The collected data is preprocessed to obtain the driving time of multiple road vehicles in a week,and this is used as the influencing factor.Reuse the collected information:the length of the intersection,the average speed of real-time vehicles at the intersection,and the length of the intersection.The first two processes of the three pre-processing processes are considered together to obtain a time-dependent factor.The correlation factors and the duration of the intersections are used to predict the results of neural network training.Based on the analysis and prediction of the data,the causes of urban traffic congestion are analyzed,and measures to reduce urban congestion are proposed.

Exploring the Development of Financial Inclusion in the Context of Financial Technology

The concept of inclusive finance was proposed and promoted by the United Nations in 2005 with the main purpose of providing services for those who lack good financial services while promoting the economic growth of family enterprises and eliminating social poverty as well as inequality.With the innovation of financial technology and its application in the field of financial inclusion,the new inclusive finance has shown strong vitality and great prospects in recent years.It provides certain ideas and directions for the development of inclusive finance in the banking industry.

An Optoelectronic thermometer based on microscale infrared-to-visible conversion devices

Thermometric detectors are crucial in evaluating the condition of target objects spanning from environments to the human body.Optical-based thermal sensing tools have received extensive attention,in which the photon upconversion process with low autofluorescence and high tissue penetration depth is considered as a competent method for temperature monitoring,particularly in biomedical fields.Here,we present an optoelectronic thermometer via infrared-to-visible upconversion,accomplished by integrated light receiving and emission devices.Fully fabricated thin-film,microscale devices present temperature-dependent light emission with an intensity change of 1.5%℃^(-1) and a spectral shift of 0.18 nm℃^(-1).The sensing mechanism is systematically characterized and ascribed to temperature dependent optoelectronic properties of the semiconductor band structure and the circuit operation condition.Patterned device arrays showcase the capability for spatially resolved temperature mapping.Finally,in vitro and in vivo experiments implemented with integrated fiber-optic sensors demonstrate real-time thermal detection of dynamic human activity and in the deep brain of animals,respectively.

Development of bioorthogonal SERS imaging probe in biological and biomedical applications

Living-cell imaging demands high specificity,sensitivity,and minimal background interference to the targets of interest.However,developing a desirable imaging probe that can possess all the above features is still challenging.The bioorthogonal surface-enhanced Raman scattering(SERS) imaging has been recently emerged through utilizing Raman reporters with characteristic peaks in Raman-silent region of cells(1800-2800 cm^(-1)),which opens a revolutionary avenue for living-cell imaging with multiplexing capability.In this review,we focus on the recent advances in the technology development and the biological and biomedical applications of the living-cell bioorthogonal SERS imaging technique.After introduction of fundamental principles for bioorthogonal tag or label,we present applications for visualization of various intracellular components and environment including proteins,nucleic acids,lipids,pH and hypoxia,even for cancer diagnosis in tissue samples.Then,various bioorthogonal SERS imaging-guided thera py strategies have been discussed such as photothera py and surge ry.In conclusion,this strategy has great potential to be a flexible and robust tool for visualization detection and diseases diagnosis.