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Assessment of Bacteriological and Metallic Contamination (Pb, Cd, As) and Analysis of Toxicological Risks in Houin Logbo (Lake Toho) in the Municipality of Lokossa
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作者 Armelle Sabine Yélignan Hounkpatin Vidédji Naéssé Adjahossou +2 位作者 Balbine Patricia Mintolé Hekpazo Zinsou Franck Mignanwandé Roch Christian Johnson 《Journal of Environmental Protection》 2021年第3期209-217,共9页
Heavy metals are dangerous pollutants for ecosystems, especially aquatic ecosystems, because of their concentration in certain living organisms and their presence in the food chain. This study aims to evaluate the bac... Heavy metals are dangerous pollutants for ecosystems, especially aquatic ecosystems, because of their concentration in certain living organisms and their presence in the food chain. This study aims to evaluate the bacteriological, metallic (Pb, Cd, As) and toxicological risks associated with houin logbo (toho lake) in the municipality of Lokossa. The results obtained concern everyone: Toho lake is contaminated by <em>Escherichia coli </em>and<em> faecal enterococci</em>, concerning the evaluation of the metallic contamination we have: water (Pb: 0.1032, Cd: 0. 046, As: 0);sediment (Pb: 14.79, Cd: 1.27, As: 0.800);<em>Oreochromis niloticus </em>(Pb: 0.143, Cd: 0.087, As: 0.466);soils (Pb: 8.528, Cd: 2.755, As: 0.833);<em>Solanum lycopersicum</em> (Pb: 0.098, Cd: 0.066, As: 0). Consumption of lake fish (<em>Oreochromis niloticus</em>) and market garden produce (<em>Solanum lycopersicum</em>) exposes populations, especially children, to the risk of As and Cd poisoning. 展开更多
关键词 Lead CADMIUM ARSENIC Toxicological Risks
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Evolution and Trend of Deep Learning in Agriculture: A Bibliometric Approach
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作者 Kimba Sabi N’goye Henoc Soude Yêyinou Laura Estelle Loko 《Journal of Computer and Communications》 2022年第12期113-124,共12页
Deep Learning has recently gained a great deal of attention. From this, resulted many applications in a variety of industries, including agriculture. An essential study goal is to understand what has been done in the ... Deep Learning has recently gained a great deal of attention. From this, resulted many applications in a variety of industries, including agriculture. An essential study goal is to understand what has been done in the use of deep learning in agriculture (DLA) thus far in order to establish a robust research agenda to address its future challenges. The present state of research on the DLA with special attention to Africa was evaluated in this study using bibliometric analysis. A search of documents dealing with DLA was realized in the Web of Science database, a world-leading publisher-independent global citation database. A bibliometric program named Bibliometrix was used to examine the data after the search yielded 3207 items. Key findings are highlighted and discussed, and then some directions for potential future research are suggested. 展开更多
关键词 Machine Learning Deep Learning AGRICULTURE BIBLIOMETRIC AFRICA
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