Publikasjoner
NIBIOs ansatte publiserer flere hundre vitenskapelige artikler og forskningsrapporter hvert år. Her finner du referanser og lenker til publikasjoner og andre forsknings- og formidlingsaktiviteter. Samlingen oppdateres løpende med både nytt og historisk materiale. For mer informasjon om NIBIOs publikasjoner, besøk NIBIOs bibliotek.
2022
Sammendrag
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Sammendrag
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Sammendrag
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Forfattere
Yi Zhang Linhui Li Zhonghao Ren Yating Yu Yeqing Li Junting Pan Yanjuan Lu Lu Feng Weijin Zhang Yongming HanSammendrag
The parameters from full-scale biogas plants are highly nonlinear and imbalanced, resulting in low prediction accuracy when using traditional machine learning algorithms. In this study, a hybrid extreme learning machine (ELM) model was proposed to improve prediction accuracy by solving imbalanced data. The results showed that the best ELM model had a good prediction for validation data (R2 = 0.972), and the model was developed into the software (prediction error of 2.15 %). Furthermore, two parameters within a certain range (feed volume (FV) = 23–45 m3 and total volatile fatty acids of anaerobic digestion (TVFAAD) = 1750–3000 mg/L) were identified as the most important characteristics that positively affected biogas production. This study combines machine learning with data-balancing techniques and optimization algorithms to achieve accurate predictions of plant biogas production at various loads.
Forfattere
David A. Robinson Attila Nemes Sabine Reinsch Alan Radbourne Laura Bentley Aidan M. KeithSammendrag
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Forfattere
Ana-Maria Pantazica Mihaela-Olivia Dobrica Catalin Lazar Cristina Scurtu Catalin Tucureanu Iuliana Caras Irina Ionescu Adriana Costache Adrian Onu Jihong Liu Clarke Crina Stavaru Norica Branza-NichitaSammendrag
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Forfattere
Daniel RasseSammendrag
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Forfattere
Yvonne RognanSammendrag
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Sammendrag
Aims Root traits associated with resource foraging, including fine-root branching intensity, root hair, and mycorrhiza, may change in soils that vary in rock fragment content (RFC), while how these traits covary at the level of individual root branching order is largely unknown. Methods We subjected two xerophytic species, Artemisia vestita (subshrub) and Bauhinia brachycarpa (shrub), to increasing RFC gradients (0%, 25%, 50%, and 75%, v v− 1) in an arid environment and measured fine-root traits related to resource foraging. Results Root hair density and mycorrhizal colonization of both species decreased with increasing root order, but increased in third- or fourth-order roots at high RFCs (50% or 75%) compared to low RFCs. The two species tend to produce more root hairs than mycorrhizas under the high RFCs. For both species, root hair density and mycorrhizal colonization intensity were negatively correlated with root length and root diameter across root order and RFCs. Rockiness reduced root branching intensity in both species comparing with rock-free soil. At the same level of RFC, A. vestita had thicker roots and lower branching intensity than B. brachycarpa and tended to produce more root hairs. Conclusion Our results suggest the high RFC soil conditions stimulated greater foraging functions in higher root orders. We found evidence for a greater investment in root hairs and mycorrhizal symbioses as opposed to building an extensive root system in rocky soils. The two species studied, A. vestita and B. brachycarpa, took different approaches to foraging in the rocky soil through distinctive trait syndromes of fine-root components.