Publications
NIBIOs employees contribute to several hundred scientific articles and research reports every year. You can browse or search in our collection which contains references and links to these publications as well as other research and dissemination activities. The collection is continously updated with new and historical material.
2025
Abstract
No abstract has been registered
Authors
David Chludil Curt Almqvist Mats Berlin Arne Steffenrem Steven E. McKeand Jiří Korecký Jan Stejskal Jaroslav Čepl Fikret Isik Debojyoti Chakraborty Silvio Schueler Torsten Pook Christi Sagariya Milan LstibůrekAbstract
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Authors
Sean P Healey Zhiqiang Yang Angela M Erb Ryan Bright Grant M Domke Tracey S Frescino Crystal B SchaafAbstract
No abstract has been registered
Abstract
Accurately predicting whether pedestrians will cross in front of an autonomous vehicle is essential for ensuring safe and comfortable maneuvers. However, developing models for this task remains challenging due to the limited availability of diverse datasets containing both crossing (C) and non-crossing (NC) scenarios. Therefore, we propose a procedure that leverages synthetic videos with C/NC labels and an untrained model whose architecture is designed for C/NC prediction to automatically produce C/NC labels for a set of real-world videos. Thus, this procedure performs a synth-to-real unsupervised domain adaptation for C/NC prediction, so we term it S2R-UDA-CP. To assess the effectiveness of S2R-UDA-CP in self-labeling, we utilize two state-of-the-art models, PedGNN and ST-CrossingPose, and we rely on the publicly-available PedSynth dataset, which consists of synthetic videos with C/NC labels. Notably, once the real-world videos are self-labeled, they can be used to train models different from those used in S2R-UDA-CP. These models are designed to operate onboard a vehicle, whereas S2R-UDA-CP is an offline procedure. To evaluate the quality of the C/NC labels generated by S2R-UDA-CP, we also employ PedGraph+ (another literature referent) as it is not used in S2R-UDA-CP. Overall, the results show that training models to predict C/NC using videos labeled by S2R-UDA-CP achieves performance even better than models trained on human-labeled data. Our study also highlights different discrepancies between automatic and human labeling. To the best of our knowledge, this is the first study to evaluate synth-to-real self-labeling for C/NC prediction.
Authors
Daniel Moreno-Fernández Johannes Breidenbach Isabel Cañellas Gherardo Chirici Giovanni D’amico Marco Ferretti Francesca Giannetti Stefano Puliti Sebastian Schnell Ross Shackleton Mitja Skudnik Iciar AlberdiAbstract
No abstract has been registered
Authors
Shaohui Zhang Poul Erik Lærke Mathias Neumann Andersen Junxiang Peng Esben Øster Mortensen Johannes Wilhelmus Maria Pullens Sheng Wang Klaus Steenberg Larsen Davide Cammarano Uffe Jørgensen Kiril ManevskiAbstract
No abstract has been registered
Abstract
No abstract has been registered
Authors
Katharina HobrakAbstract
No abstract has been registered
Authors
Arne Verstraeten Andreas Schmitz Bernd Ahrends Nicholas Clarke Wim de Vries Karin Hansen Char Hilgers Carmen Iacoban Tamara Jakovljevic Per Erik Karlsson Till Kirchner Aldo Marchetto Henning Meesenburg Gunilla Pihl Karlsson Anne-Katrin Prescher Anne Thimonier Peter WaldnerAbstract
No abstract has been registered
Authors
Aline Roma Tomaz William Ramos da Silva Thiago Inagaki Emylaine Oliveira Santos Giselle Gomes Monteiro Fracetto Felipe José Cury Fracetto Vitor da Silveira Freitas Diego Victor Babos Débora Milori Abelardo Antônio de Assunção Montenegro Ademir De Oliveira FerreiraAbstract
Knowledge gaps persist regarding mulch decomposition, nutrient dynamics, and microbial responses in semi-arid soils under reclaimed water irrigation. This is a critical issue for water-scarce regions like the Brazilian semi-arid. This study evaluated these processes in cactus-sorghum intercropping systems with mulch under irrigation depths with reclaimed water. The study employed a randomized block design with four replicates, testing irrigation depths of 0 %, 80 %, 100 %, and 120 % of sorghum ETc. Mulch decomposition was monitored for 165 days using litter bags, with subsequent C/N analysis of residual biomass. Soils at 0–0.10 m and 0.10–0.20 m depths were sampled to determine the contents of NO₃−, NH₄+, P, and microbial biomass C (MB-C), basal soil respiration, and aromatization index (ALIFS). Decomposition revealed the highest rates at 10 days (16 %) under 80 % ETc and at 25 days (24 %, 22 %, and 21 %) under 80 %, 100 %, and 120 % ETc, surpassing non-irrigated soils. Residue half-life was 182–196 days. Mulch N content declined most sharply at 10 days (1.2–1.8 g kg−1 in irrigated treatments). Soil NH₄+ and NO₃− peaked in the 0–0.10 m layer, but nitrate decreased by 15–62 % at 65 days, signaling microbial disruption from water excess. MB-C dropped >90 % at 65 days but recovered by 165 days, with the 80 % and 100 % treatments showing the highest MB-C in surface soils. These treatments also increased available P₂O₅ by 46–216 mg kg−1 versus 0 % and 120 % ETc. The ALIFS was higher in irrigated soils, especially at 120 % ETc (0–0.10 m). Reclaimed water irrigation enhanced nutrient supply, decomposition, and microbial activity, reducing synthetic fertilizer dependency while improving soil health in semi-arid agroecosystems.