Results 81 to 90 of about 3,482,440 (295)

IMPACTS OF METEOROLOGICAL VARIABLES ON NET ECOSYSTEM EXCHANGE OF WINTER WHEAT

Proceedings, 2015
Understanding the characterization of net ecosystem exchange (NEE) with atmospheric variables is crucial. Measured NEE of CO2 between the atmosphere and different ecosystems helps to understand the CO2 balance and its component (gross primary production (GPP) and ecosystem respiration (Reco).
Serhan Yeşilköy, Levent Şaylan, Barış Çaldağ   +2 more
openaire   +1 more source

Comparing observations and process-based simulationsof biosphere-atmosphere exchanges on multiple timescales [PDF]

, 2010
Terrestrial biosphere models are indispensable tools for analyzing the Biosphere-atmosphere exchange of carbon and water. Evaluation of these models using site level observations scrutinizes our current understanding of biospheric responses to ...
Lange, H., Braakhekke, M.C., Reichstein, M., Jung, Martin, Carvalhais, N., Moors, E., Beer, C., Seneviratne, S.I., Jung, M., Reichstein, Markus, Zaehle, S., Seneviratne, Sonia I., Carvalhais, Nuno, Le Maire, Guerric, Beer, Christian, Seneviratne, S., Braakhekke, M., Mahecha, Miguel D., Mahecha, M., Mahecha, M.D., Braakhekke, M. C., Seneviratne, S. I., Lange, Holger, Moors, E.J., Zaehle, Sönke, Le Maire, G., Moors, Eddy J., Maire, G., Le, Seneviratne, Sonia I.; id_orcid, Mahecha, M. D., Maire, G. le   +30 more
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Sustainable Materials Design With Multi‐Modal Artificial Intelligence

Advanced Science, EarlyView.
Critical mineral scarcity, high embodied carbon, and persistent pollution from materials processing intensify the need for sustainable materials design. This review frames the problem as multi‐objective optimization under heterogeneous, high‐dimensional evidence and highlights multi‐modal AI as an enabling pathway.
Tianyi Xu, Tianshuo Wei, Yan Ge, Bo Peng, Yue Li, Maolin Wang, Peng Wen, Chao Yang, Ye Wei   +8 more
wiley   +1 more source

The Loobos ecosystem first tower dataset: meteorology, turbulent fluxes and net ecosystem exchange (1996 to 2021) [PDF]

Earth System Science Data
We describe a 25 years (1996–2021) observational dataset of meteorology, turbulent fluxes and net ecosystem exchange collected from the first tower at the Loobos site, the Netherlands (NL). This is one of the 17 first FLUXNET sites globally.
H. Zhao, H. Dolman, J. Elbers, W. Jans, B. Kruijt, E. Moors, H. Snellen, J. Vila-Guerau de Arellano, W. Peters, W. Peters, M. C. Krol, R. Hutjes, M. van der Molen   +12 more
doaj   +1 more source

Appendix D. Test results of the repeated-measures ANOVA on net ecosystem CO2 exchange during the drought period.

, 2016
Test results of the repeated-measures ANOVA on net ecosystem CO2 exchange during the drought ...
Bjorn J. M. Robroek (655384), Wolf M. Mooij (131136), Jan J. Kuiper (2927088), Luca Bragazza (2927085)   +3 more
core   +1 more source

Functional Disorder at the Neural Interface: How Disordered Nanostructures Promote Proper Growth and Differentiation in In Vitro Neural Cultures

Advanced Science, EarlyView.
This work provides a practical guide for neuroengineers to design advanced neural interfaces, embracing and tailoring the concept of functional disorder. By bridging 2D and 3D in vitro models, this work highlights how non‐periodic, spatially heterogeneous, multiscale nanotopography can enable more physiologically relevant platforms for studying neural ...
F. Maita, E. Palmieri, I. Lucarini, L. Montaina, L. Maiolo   +4 more
wiley   +1 more source

Influence of the Asian monsoon on net ecosystem carbon exchange in two major ecosystems in Korea [PDF]

Biogeosciences, 2010
Abstract. Considering the feedback in radiation, temperature, and soil moisture with alterations in rainfall patterns, the influence of the changing monsoon on Net Ecosystem CO2 Exchange (NEE) can be critical to the estimation of carbon balance in Asia.
H. Kwon, J. Kim, J. Hong, J.-H. Lim
openaire   +3 more sources

Modeling Terrestrial Net Ecosystem Exchange Based on Deep Learning in China

Remote Sensing
In estimating the global carbon cycle, the net ecosystem exchange (NEE) is crucial. The understanding of the mechanism of interaction between NEE and various environmental factors of ecosystems has been very limited, and the interactions between the ...
Zeqiang Chen, Lei Wu, Nengcheng Chen, Ke Wan   +3 more
doaj   +1 more source

Deep Learning‐Driven Discovery and Engineering of an Efficient PETase for Depolymerization and Detoxification of PET Microplastics Under Physiological Conditions

Advanced Science, EarlyView.
A deep learning–driven pipeline mining 246 million protein sequences uncovers AhPETase, an evolutionarily distinct PET hydrolase. Engineered variant AhPETaseM1 degrades post‐consumer PET microplastics under physiological conditions and reverses microplasticinduced cytotoxicity in human lung and colon cells, establishing enzymatic microplastic ...
Yuxuan Wang, Shijie He, Yuheng Chang, Sheng Mao, Jianan Canal Li, Binbin Chen, Hongxun Gao, Mingchun Xu, Chenxu Liu, Yajie Wang   +9 more
wiley   +1 more source

Toward a consistency cross-check of eddy covariance flux-based and biometric estimates of ecosystem carbon balance [PDF]

, 2009
Quantification of an ecosystem's carbon balance and its components is pivotal for understanding both ecosystem functioning and global cycling. Several methods are being applied in parallel to estimate the different components of the CO2 balance. However,
Schulze, E., Janssens, I.A., Schulze, E.D., Linder, S.K., Schulze, E.-D., Janssens, I., Goulden, Michael L., Reichstein, M., Kutsch, W.L., Papale, D., Saunders, M., Law, B. E., Matteucci, G, Kutsch, Werner L., Moors, E., Munger, J. W., Falge, E., Janssens, I. A., Munger, J.W., Janssens, Ivan A., Moors, E.; id_orcid, Law, B., Kutsch, W., Munger, J. William, Law, Beverly E., Pilegaard, K., Dragoni, D., Granier, A., Linder, S., Kutch, W.L., Falge, E.M., Goulden, M. L., Falge, E. M., Luyssaert, S.; id_orcid, Munger, J., Luyssaert, S., Law, B.E., Goulden, M., Moors, E.J., Kutsch, W. L., Goulden, M.L., Matteucci, G., Saunders M, Pilegaard K, Reichstein, M, Luyssaert, S, Law BE, Linder S, Reichstein M, Falge E M, Kutsch WL, Swedish University of Agricultural Sciences., Schulze ED, Luyssaert S, Sveriges lantbruksuniversitet., Scxhulze, E.D., Matteucci G, Munger JW, Granier A, Papale D, Janssens IA, Moors E, Goulden M, Linder, Sune,, Dragoni D   +64 more
core   +1 more source