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.
2024
Sammendrag
Det er ikke registrert sammendrag
Forfattere
Marie-Christin Wimmler Nadezhda Nadezhdina Hannah Bowen Susana Alvarado-Barrientos Teresa David Gabriela Fontenla-Razzetto Britt Kniesel Holger Lange Roman Mathias Link Yang Liu Jorge López-Portillo Clara Pinto Junbin Zhao Alejandra G. VovidesSammendrag
1. Sap flow measurements are fundamental to understanding water use in trees and could aid in predicting climate change effects on forest function. Deriving knowledge from such measurements requires empirical calibrations and upscaling methods to translate thermometric recordings to tree water use. Here, we developed a user-friendly open-source application, the Sap Flow Analyzer (SFA), which estimates sap flow rates and tree water use from the heat field deformation (HFD) instruments. 2. The SFA incorporates four key features to ensure maximum accuracy and reproducibility of sap flow estimates: diagnosis diagrams to assess data patterns visually, regression models implemented to increase accuracy when estimating K (the main HFD parameter), three approaches to upscale sap flow rates to whole-tree water use and visualization of the input parameters' uncertainty. Thirteen participants were given three raw datasets and assigned data processing tasks using the SFA user guide, from estimating sapwood depth to scaling sap flow rates to whole-tree water use to assess the reproducibility and applicability of the SFA. 3. Participants' results were reasonably consistent and independent of their background in using the SFA, R, or HFD method. The results showed lower variability for high flow rates (SD: mean 1% vs. 10%). K estimates and sapwood depth differentiation were the primary sources of variability, which in turn was mainly caused by the user's chosen scaling method. 4. The SFA provides an easy way to visualize and process sap flow and tree water use data from HFD measurements. It is the first free and open software tool for HFD users. The ability to trace analysis steps ensures reproducibility, increasing transparency and consistency in data processing. Developing tools such as the SFA and masked trials are essential for more precise workflows and improved quality and comparability of HFD sap flow datasets.
Forfattere
Junbin ZhaoSammendrag
Det er ikke registrert sammendrag
Forfattere
Anastasia Georgantzopoulou Sebastian Kühr Ralph Kaegi Mark Rehkämper Ralph A. Sperling Karl Andreas Jensen Ana Catarina Almeida Claire CoutrisSammendrag
Det er ikke registrert sammendrag
Sammendrag
Det er ikke registrert sammendrag
Sammendrag
Det er ikke registrert sammendrag
Sammendrag
Det er ikke registrert sammendrag
Sammendrag
Det er ikke registrert sammendrag
Sammendrag
Det er ikke registrert sammendrag
Forfattere
Yi Zhang Xingru Yang Yijing Feng Zhiyue Dai Zhangmu Jing Yeqing Li Lu Feng Yanji Hao Shasha Yu Weijin Zhang Yanjuan Lu Chunming Xu Junting PanSammendrag
Exploring the complex mechanism of anaerobic digestion with hydrothermal pretreatment (HTAD) for biomass efficiently and optimising the reaction conditions are critical to improving the performance of methane production. This study used H2O automated machine learning (AutoML) for comprehensive prediction, analysis, and targeted optimization of the HTAD system. An IterativeImputer system for data filling was constructed. The comparison of three basic regressors showed that random forest performed optimally for filling (R2 > 0.95). The gradient boosting machine (GBM) model was searched by H2O AutoML to show optimal performance in prediction (R2 > 0.96). The software was developed based on the GBM model, and two prediction schemes were devised. The generalization error of the software was less than 10%. The Shapley Additive exPlanations value showed that solid to liquid ratio, hydrothermal pretreatment (HT) temperature, and particle size have greater potential for improving cumulative methane production (CMP). A Bayesian-HTAD optimization strategy was devised, using the Bayesian optimization to directionally optimize the reaction conditions, and performing experiments to validate the results. The experimental results showed that the CMP was significantly improved by 51.63%. Compared to the response surface methodology, the Bayesian optimization relatively achieved a 2.21–2.50 times greater effect. Mechanism analyses targeting the experiments showed that HT was conducive to improving the relative abundance of Sphaerochaeta, Methanosaeta, and Methanosarcina. This research achieved accurate prediction and targeted optimization for the HTAD system and proposed multiple filling, prediction, and optimization strategies, which are expected to provide an AutoML optimization paradigm for anaerobic digestion in the future.