Journal of Atomic, Molecular, Condensed Matter and Nano Physics最新文献_第4页

An Investigation of Optical Gain of Nanomaterial AlGaAsIn/InP under CTLSs in Optical Communications 光通信中纳米材料AlGaAsIn/InP在ctls下的光增益研究

Journal of Atomic, Molecular, Condensed Matter and Nano Physics Pub Date : 2020-12-31 DOI: 10.26713/JAMCNP.V7I3.1544

P. Lal

引用次数: 0

Synthesis and Characterization of Cu-Doped SnO(_2) (Sn(_{0.98})Cu(_{0.02})O(_2)) Thin Film by Sol-gel Technique for LPG Sensing 溶胶-凝胶法制备LPG传感用Cu掺杂SnO (_2) (Sn (_{0.98}) Cu (_{0.02}) O (_2))薄膜及表征

Journal of Atomic, Molecular, Condensed Matter and Nano Physics Pub Date : 2020-12-31 DOI: 10.26713/jamcnp.v7i3.1539

Bouabida Seddik, Benkara Salima, Ghamri Houda, Fares Abdelhakim

引用次数: 0

Energy Efficiency in Buildings: Numerical Study of the Impact of Integrating Phase Change Materials Into the Walls 建筑物的能源效率:将相变材料集成到墙壁中的影响的数值研究

Journal of Atomic, Molecular, Condensed Matter and Nano Physics Pub Date : 2020-12-31 DOI: 10.26713/jamcnp.v7i3.1548

Zakaria Ouaouja, A. Ousegui

引用次数: 0

The Use of a Recurrent Neural Network for Forecasting Ozone Concentrations in the City of Agadir (Morocco) 使用递归神经网络预测阿加迪尔市(摩洛哥)臭氧浓度

Journal of Atomic, Molecular, Condensed Matter and Nano Physics Pub Date : 2020-12-31 DOI: 10.26713/JAMCNP.V7I3.1545

Anas Adnane, Redouane Leghrib, J. Chaoufi, A. Chirmata

{"title":"The Use of a Recurrent Neural Network for Forecasting Ozone Concentrations in the City of Agadir (Morocco)","authors":"Anas Adnane, Redouane Leghrib, J. Chaoufi, A. Chirmata","doi":"10.26713/JAMCNP.V7I3.1545","DOIUrl":"https://doi.org/10.26713/JAMCNP.V7I3.1545","url":null,"abstract":"Air quality is a complex issue which depends mutually on source emission, land topography, meteorological parameters and used mathematical tools for forecasting its dispersion. One of major toxic gas is ozone which could be dangerous to human health. The present work has been developed to forecast ozone concentrations in the city of Agadir using a Recurrent Neural Network (RNN). Predicting ozone concentrations will provide useful information, especially, for decision makers in order to prevent and reduce the ozone human health impacts. The data was collected in the most polluted area in the city using a mobile monitoring station during a period of 60 days. We have tested different neural network architectures and we found that the 1-hour forecast model, whose input parameters are a combination of meteorological parameters as well as CO and SO(_2), give the most optimal results. Coefficient of Correlation (CC), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) were used to evaluate the statistical agreement between observed and predicted values. The model successfully predicts the ozone concentration by a bias of 4 (mu)g.m(^{-3}) over 24 hours and which a correlation coefficient is more than 80%. This work highlights the ability of the recurrent neural networks to forecast air pollutant concentrations in urban areas.","PeriodicalId":300387,"journal":{"name":"Journal of Atomic, Molecular, Condensed Matter and Nano Physics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121222324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Far Field Prediction From Near Field Using the Plane Wave Spectrum Method 用平面波谱法从近场预测远场

Journal of Atomic, Molecular, Condensed Matter and Nano Physics Pub Date : 2020-12-31 DOI: 10.26713/JAMCNP.V7I3.1542

Mohamed Amine Benchana, Abderrezak Khalfallaoui, A. Chaabane, A. Babouri, A. Ladjimi, Z. Riah

引用次数: 0