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| 1 | 澳门地区原水及海水中的病原虫调查显示文摘贾第鞭毛虫和隐孢子虫是水源性流行病中常见的病原体 ,通常以孢囊或卵囊的形式存在于水中 ,常用消毒剂对其消毒效果较差。我国目前尚无饮用水的病原虫标准 ,有关病原虫在我国饮用水系统中存在情况的调查也未见报道。澳门自来水公司化验研究中心在已完全掌握了病原虫的检测方法后 ,对澳门地区供水系统及海水水样中的病原虫现状进行了调查。结果显示 ,在受到污染的原水中有病原虫检出。 | 范晓军 陈佩堂 陈成章 Albinet F | 2001 | 中国给水排水2001，17，11： | 22 |
| 2 | An optimized and standardized test to determine thepresence of the protozoa Cryptosporidium and Giardia in water显示文摘 | Stanfield G Carrington E Albinet F | 2000 | Water Sci Technol2000，41，7： | 1 |
| 3 | Cartographie de la Vulnerabilit a la Polution des Nappes d' eau Souterraine 显示文摘 | Albinet M Margat J | 1970 | Bull BRGM 2~:me sirie1970，3，4： | 1 |
| 4 | A new ozone denuder for aerosol sampling based on an ionic liquid coating显示文摘 | Albinet A Papaiconomou N Estager J | 2010 | Analytical and Bioanalytical Chemistry2010，397，2： | 1 |
| 5 | Increased heart rate var- iability and executive performance after aerobic training in the elderly 显示文摘 | Albinet C T Boucard G Bouquet C A | 2010 | Eur J Appl Physiol2010，109，4： | 1 |
| 6 | Polycyclic aromatic hydrocarbons (PAHs),nitrated PAHs and oxygenated PAHs in ambient air of the Marseilles area (South of France):concentrations and sources显示文摘 | Albinet A Leoz-Garziandia E Budzinski H | | 0，，1： | 1 |
| 7 | Processing speed and executive functions in cognitive aging : how to disentangle their mu- tual relationship显示文摘 | ALBINET C T BOUCARD G BOUQUET C A | 2012 | Brain and Cognition2012，79，1： | 1 |
| 8 | Cartographie de la Vulnerabilit~ a la Pollution des Nappesd'eau Souterraine 显示文摘 | Albinet M Margat J | 1970 | Bull BRGM 2~me s6rie1970，3，4： | 1 |
| 9 | Cartographie de la vulnerabilite a la pollution des nappes d'eau souterraine 显示文摘 | Albinet M Margat J | 1970 | Bulletin BRGM 2nd Series1970，3，4： | 1 |
| 10 | An optimised and standardized test to determine the presence of the protozoa Cryptosporidium and Giardia in water显示文摘 | Stanfield G Carrington E Albinet F | 2000 | Water Science and Technology2000，41，： | 1 |
| 11 | Simultaneous analysis of oxygenated and nitrated polycyclic aromatic hhydrocarbons on standard reference material 1649a (Urban Dust) and on natural ambient air samples by gas chromatography-mass spectrometry with negative ion chemical ionisation显示文摘 | Albinet A Leoz-Garziandia E Budzinski H | 2006 | J Chromatogr A2006，1121，1： | 1 |
| 12 | Increased heart rate variability and executive performance after aerobic training in the elderly显示文摘 | Albinet CT Boucard G Bouquet CA | 2010 | Eur J Appl Physiol2010，109，4： | 1 |
| 13 | Cartographie de la vulndrabilite/t la pollution des nappes d'eau souterraine 显示文摘 | Albinet M Margat J | 1970 | Bull BRGM 2eme serie1970，3，4： | 1 |
| 14 | Cartogrphic de la vulnérabilitéà la pollution des nappes d'au souterraine显示文摘 | ALBINET M MARGAT J | 1970 | Bull BRGM1970，2，4： | 1 |
| 15 | Cartographie de la vulnerabilit6 a la pollution des nappes dau souterraine显示文摘 | ALBINET M MARGAT J | 1970 | Bull BRGM 2me s6fie1970，3，4： | 1 |
| 16 | Simultaneous analysis of oxygenated and nitrated poly- cyclic aromatic hydrocarbons on standard reference materi- al 1 649 (a urban dust)and on natural ambient air samples by gas chromatography-mass spectrometry with negative ion chemical ionization 显示文摘 | ALBINET A LEOZ -GARZIANDIA E BUDZINSKI H | 2006 | J Chromatogr A2006，11，21： | 1 |
| 17 | 遥感作物制图辅助核事故农业风险决策显示文摘【目的】将作物时空分布数据应用于核事故农业风险决策支持系统,体现遥感作物制图在核事故农业风险决策中的重要性。【方法】文章以大亚湾核电基地为研究案例,对其周边地区的作物轮作系统进行遥感制图;作物时空分布数据经后处理,上传至核事故农业风险决策支持系统,实现作物样本任务的自动生成,以及放射性核素浓度的时空分布模拟。【结果】提出的遥感制图方法可以在耕地破碎、云雨繁密区识别作物轮作系统,快速、准确地提供大范围作物时空分布数据。经过处理的作物时空分布数据,能够方便地应用于决策支持系统,辅助完成特定或优先区作物样本任务点的自动生成,以及放射性核素浓度时空分布的模拟。【结论】遥感作物制图与核事故农业风险决策支持系统相结合,可进一步提高采样的有效性,提升放射性核素空间和时间分布模拟与预测的准确性。从而帮助决策者制定核污染监测和评估策略、修复计划,科学指导农业生产的恢复。未来,有必要深入研究遥感作物制图在核事故农业风险决策中的应用,充分发挥遥感技术与数据的优势,规避核事故对农业生产带来的风险。 | 刘园 Lazar Adjigogov Franck Albinet Gerd Dercon 余强毅 吴文斌 周清波 | 2022 | 中国农业信息2022，34，1： | 0 |
| 18 | Prediction of exchangeable potassium in soil through mid-infrared spectroscopy and deep learning:From prediction to explainability显示文摘The ability to characterize rapidly and repeatedly exchangeable potassium(Kex)content in the soil is essential for optimizing remediation of radiocaesium contamination in agriculture.In this paper,we show how this can be now achieved using a Convolutional Neural Network(CNN)model trained on a large Mid-Infrared(MIR)soil spectral library(40,000 samples with Kex determined with 1 M NH4OAc,pH 7),compiled by the National Soil Survey Center of the United States Department of Agriculture.Using Partial Least Squares Regression as a base-line,we found that our implemented CNN leads to a significantly higher prediction performance of Kex when a large amount of data is available(10000),increasing the coefficient of determination from 0.64 to 0.79,and reducing the Mean Absolute Percentage Error from 135%to 31%.Furthermore,in order to provide end-users with required interpretive keys,we implemented the GradientShap algorithm to identify the spectral regions considered important by the model for predicting Kex.Used in the context of the implemented CNN on various Soil Taxonomy Orders,it allowed(i)to relate the important spectral features to domain knowledge and(ii)to demonstrate that including all Soil Taxonomy Orders in CNN-based modeling is beneficial as spectral features learned can be reused across different,sometimes underrepresented orders. | Franck Albinet Yi Peng Tetsuya Eguchi Erik Smolders Gerd Dercon | 2022 | Artificial Intelligence in Agriculture2022，，1： | 0 |
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