带电更换500kV同塔双回输电线路绝缘子工具研制

随着500kV交流紧凑型线路相继投入运行,在其带电作业的部分停电检修方式、临界距离时机械装置代替作业人员进入电场开展带电作业方式的研究以及带电作业相关标准制定方面,其研究十分有限。为此,本文通过对架空导线的垂直荷载和水平张力进行计算分析,设计了一套用于带电更换500kV同塔双回输电线路绝缘子的成套工具。通过对各部分力学校核分析,证明设计方案安全可靠,可满足作业要求。最后,成套工具在现场进行了应用,证明了工具的实用性,有效提高了作业效率。

IGF2BPs as novel m^(6)A readers:Diverse roles in regulating cancer cell biological functions,hypoxia adaptation,metabolism,and immunosuppressive tumor microenvironment

m^(6)A methylation is the most frequent modification of mRNA in eukaryotes and plays a crucial role in cancer progression by regulating biological functions.Insulin-like growth factor 2 mRNA-binding proteins(IGF2BP)are newly identified m^(6)A‘readers’.They belong to a family of RNA-binding proteins,which bind to the m^(6)A sites on different RNA sequences and stabilize them to promote cancer progression.In this review,we summarize the mechanisms by which different upstream factors regulate IGF2BP in cancer.The current literature analyzed here reveals that the IGF2BP family proteins promote cancer cell proliferation,survival,and chemoresistance,inhibit apoptosis,and are also associated with cancer glycolysis,angiogenesis,and the immune response in the tumor microenvironment.Therefore,with the discovery of their role as‘readers’of m^(6)A and the characteristic re-expression of IGF2BPs in cancers,it is important to elucidate their mechanism of action in the immunosuppressive tumor microenvironment.We also describe in detail the regulatory and interaction network of the IGF2BP family in downstream target RNAs and discuss their potential clinical applications as diagnostic and prognostic markers,as well as recent advances in IGF2BP biology and associated therapeutic value.

Predicting PM_(2.5) Concentration in the Yangtze River Delta Region Using Climate System Monitoring Indices and Machine Learning

Changing meteorological conditions during autumn and winter have considerable impact on air quality in the Yangtze River Delta(YRD)region.External climatic factors,such as sea surface temperature and sea ice,together with the atmospheric circulation,directly affect meteorological conditions in the YRD region,thereby modulating the variation in atmospheric PM_(2.5) concentration.This study used the evolutionary modeling machine learning technique to investigate the lag relationship between 144 climate system monitoring indices and autumn/winter PM_(2.5) concentration over 0-12 months in the YRD region.After calculating the contribution ratios and lagged correlation coefficients of all indices over the previous 12 months,the top 36 indices were selected for model training.Then,the nine indices that contributed most to the PM_(2.5) concentration in the YRD region,including the decadal oscillation index of the Atlantic Ocean and the consistent warm ocean temperature index of the entire tropical Indian Ocean,were selected for physical mechanism analysis.An evolutionary model was developed to forecast the average PM_(2.5) concentration in major cities of the YRD in autumn and winter,with a correlation coefficient of 0.91.In model testing,the correlation coefficient between the predicted and observed PM_(2.5) concentrations was in the range of 0.73-0.83 and the root-mean-square error was in the range of 9.5-11.6μg m-3,indicating high predictive accuracy.The model performed exceptionally well in capturing abnormal changes in PM_(2.5) concentration in the YRD region up to 50 days in advance.