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Contrasting Regional Responses of Indian Summer Monsoon Rainfall to Exhausted Spring and Concurrently Emerging Summer El Nino Events

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Advances in Atmospheric Sciences 2023年第4期40卷 697-710页

作者： E.K.KRISHNA KUMAR S.ABHILASH sankar syam P.VIJAYKUMAR K.R.SANTOSH A.V.SREENATH Department of Atmospheric Sciences Cochin University of Science and TechnologyCochin 682022India Advanced Centre for Atmospheric Radar Research Cochin University of Science and TechnologyCochin 682022India Faculty of Science and Natural Resources Universiti Malaysia SabahKota Kinabalu 88400Malaysia

The inverse relationship between the warm phase of the El Nino Southern Oscillation(ENSO)and the Indian Summer Monsoon Rainfall(ISMR)is well ***,some El Nino events that occur in the early months of the year(boreal spring)transform into a neutral phase before the start of summer,whereas others begin in the boreal summer and persist in a positive phase throughout the summer monsoon *** study investigates the distinct influences of an exhausted spring El Nino(springtime)and emerging summer El Nino(summertime)on the regional variability of *** two ENSO categories were formulated based on the time of occurrence of positive SST anomalies over the Nino-3.4 region in the *** ISMR’s dynamical and thermodynamical responses to such events were investigated using standard metrics such as the Walker and Hadley circulations,vertically integrated moisture flux convergence(VIMFC),wind shear,and upper atmospheric *** monsoon circulation features are remarkably different in response to the exhausted spring El Nino and emerging summer El Nino phases,which distinctly dictate regional rainfall *** dynamic and thermodynamic responses reveal that exhausted spring El Nino events favor excess monsoon rainfall over eastern peninsular India and deficit rainfall over the core monsoon regions of central *** contrast,emerging summer El Nino events negatively impact the seasonal rainfall over the country,except for a few regions along the west coast and northeast India.

关键词： exhausted spring El Nino emerging summer El Nino Indian Summer Monsoon Hadley and Walker circulation tropical easterly jet vertical integrated moisture flux convergence

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Stock Market Trading Based on Market Sentiments and Reinforcement Learning

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Computers, Materials & Continua 2022年第1期70卷 935-950页

作者： K.M.Ameen Suhail syam sankar Ashok S.Kumar Tsafack Nestor Naglaa F.Soliman Abeer D.Algarni Walid El-Shafai Fathi E.Abd El-Samie Department of Computer Science&Engineering NSS College of EngineeringPalakkad678008KeralaIndia Department of Electronics and Communication Engineering NSS College of EngineeringPalakkad678008KeralaIndia Unitéde Recherche de Matière Condensée D’Electronique et de Traitement du Signal(URAMACETS)Department of PhysicsUniversity of DschangP.O.Box 67DschangCameroon Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia Department Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt

Stock market is a place,where shares of different companies are *** is a collection of buyers’and sellers’*** this digital era,analysis and prediction in the stock market have gained an essential role in shaping today’s *** market analysis can be either fundamental or *** analysis can be performed either with technical indicators or through machine learning *** this paper,we report a system that uses a Reinforcement Learning(RL)network and market sentiments to make decisions about stock market *** system uses sentiment analysis on daily market news to spot trends in stock *** sentiment analysis module generates a unified score as a measure of the daily news about *** score is then fed into the RL module as one of its *** RL section gives decisions in the form of three actions:buy,sell,or *** objective is to maximize long-term future *** have used stock data of Apple from 2006 to 2016 to interpret how sentiments affect *** stock price of any company rises,when significant positive news become available in the public *** results reveal the influence of market sentiments on forecasting of stock prices.

关键词： Deep learning machine learning daily market news reinforcement learning stock market

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