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Vêtements électriques

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法语国家与地区研究 2008年第6期 54-57页

作者： Boris bellanger

Recharger son portable ou son iPod en ... marchant ? Grce à des nanofibres convertissant l’énergie mécanique en énergie électrique, ce sera bient?t possible. Ne reste plus qu’ à améliorer le rendement.

关键词： Wang lectriques tements iPod

沙鼠精囊中的雄激素依赖性分泌蛋白POSVP21证实为转胶蛋白

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Asian Journal of Andrology 2010年第3期12卷 422-430,I0012页

作者： Naima Kaci-Ouchfoun Anne Incamps Fatima Hadj-Bekkouche Mohamed CherifAbbadi Laurent bellanger Therese Gernigon-Spychalowicz Laboratory of Arid Areas Biological Sciences Institute USTHB BP 132 El Alia 16100 Algiers Algeria Department of biochemistry and nuclear toxicology （SBTN） CEA Marcoule BP 17171. F-30207 Bagnols-sur-Ceze France Laboratory of Biology and Organisms Physiology USTHB BP 132 El Alia 16100Algiers Algeria Department of Immunology Algerian Pasteur Institute 16000 Algiers Algeria

The seminal vesicles of adult sand rat contain a major secretory protein band （MW 21 kDa） designated as Psammomys obesus seminal vesicles protein of 21 kDa （POSVP21）. This protein is abundant in secretions, regulated by androgens and also present in the vaginal plug. POSVP21 accounts for over 22.3% of soluble proteins from homogenate during the breeding season, 13.3% during the middle season and 5.3% during the hormonal regression season. It is absent during the non-breeding season. POSVP21 is localized in the cytoplasm of epithelial cells and in secretory products in the lumen. It presents an immunological homology with two epididymal proteins with the same molecular weight and a high degree of homology with transgelin from rat （Rattus norvegicus）.

关键词： androgen-dependent Psammomys obesus seminal vesicles transgelin

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重访通古斯大爆炸

新安全东方消防 2009年第1期 24-29页

作者： Boris bellanger 张艳

1908年6月30日清晨,在俄罗斯帝国西伯利亚森林的石泉通古斯卡河畔,晨曦微露,晓雾初开。霎时,一个神秘的巨型火球划破苍穹,临空爆炸。其声震天撼地,其亮度与太阳相若。干燥的强风夹杂着巨大的噪音横扫大地,几千万棵树木顷刻间被全部推倒... 详细信息

1908年6月30日清晨,在俄罗斯帝国西伯利亚森林的石泉通古斯卡河畔,晨曦微露,晓雾初开。霎时,一个神秘的巨型火球划破苍穹,临空爆炸。其声震天撼地,其亮度与太阳相若。干燥的强风夹杂着巨大的噪音横扫大地,几千万棵树木顷刻间被全部推倒……这场发生在俄罗斯西伯利亚中部的灾难距今已有百年,却仍然令各国的科学家百思不解。

关键词：通古斯大爆炸残骸俄罗斯帝国西伯利亚森林弗拉迪斯威特通古斯之谜俄罗斯天体碰撞坑冲击地貌陨石坑库利克

资源警报！铜、黄金、锌、铂、铀、磷……26种矿物即将枯竭！

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新发现 2012年第8期 30-49页

作者： Boris bellanger Viocent Nouyrigat

这是一道全人类享用至今的大餐：准得一颗直径13000千米的岩石行星；把88种化学元素做成馅料填进它的地壳；充分搅拌这些馅料，形成4000余种矿物质；再浇上30000亿桶唾手可得的石油……瞧，我们就有一席可供几十亿人欢享的地下资源盛宴。

关键词：地下资源矿物质警报黄金化学元素铜锌铂

Comparison of Different Regularized and Shrinkage Regression Methods to Predict Daily Tropospheric Ozone Concentration in the Grand Casablanca Area

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Advances in Pure Mathematics 2018年第10期8卷 793-812页

作者： Halima Oufdou Lise bellanger Amal Bergam Angélina El Ghaziri Kenza Khomsi El Mostafa Qannari Laboratory MAE2D University of Abdelmalek Essaadi Larache Morocco Laboratory of Mathematics Jean Leray UMR CNRS 6629 University of Nantes Nantes France Maison des Sciences de l’Homme Ange Guépin USR 3491 University of Nantes Nantes France National Meteorological Office Casablanca Morocco StatSC ONIRIS INRA Nantes France

Tropospheric ozone (O3) is one of the pollutants that have a significant impact on human health. It can increase the rate of asthma crises, cause permanent lung infections and death. Predicting its concentration levels is therefore important for planning atmospheric protection strategies. The aim of this study is to predict the daily mean O3 concentration one day ahead in the Grand Casablanca area of Morocco using primary pollutants and meteorological variables. Since the available explanatory variables are multicollinear, multiple linear regressions are likely to lead to unstable models. To counteract the multicollinearity problem, we compared several alternative regression methods: 1) Continuum Regression;2) Ridge & Lasso Regressions;3) Principal component regression (PCR);4) Partial least Square regression & sparse PLS and;5) Biased Power Regression. The aim is to set up a good prediction model of the daily ozone in the Grand Casablanca area. These models are fitted on a training data set (from the years 2013 and 2014), tested on a data set (from 2015) and validated on yet another data set data (from 2015). The Lasso model showed a better performance for the prediction of ozone concentrations compared to multiple linear regression and its other alternative methods.

关键词： Multiple Linear Regression Multicollinearity Penalized Regression Statistical Forecasting Tropospheric Ozone