Naoyuki Yamashita
Biographic Data
| ID | 5809763 |
|---|---|
| NAME | Naoyuki Yamashita |
| GIVEN NAMES | Naoyuki |
| FAMILY NAME | Yamashita |
| SIGNATURE | YAMASHITA N |
| AFFILIATIONS | Kyoto University |
| ORCID | 0000-0003-3398-6825 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2018 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
Mining global soil carbon datasets
The future of terrestrial soil carbon stocks plays a crucial role in climate change prediction. Modern machine learning techniques are now widely applied in soil science to predict the spatial distribution of soil properties from observational data. Beyond prediction, the use of machine learning as a data-mining tool offers a promising pathway for improving soil carbon modelling and refining projections of climate–carbon feedbacks. In this paper,…
Removal Characteristics of N-Nitrosamines and Their Precursors by Pilot-Scale Integrated Membrane Systems for Water Reuse
This study investigated the removal characteristics of N -Nitrosamines and their precursors at three pilot-scale water reclamation plants. These plants applies different integrated membrane systems: (1) microfiltration (MF)/nanofiltration (NF)/reverse osmosis (RO) membrane; (2) sand filtration/three-stage RO; and (3) ultrafiltration (UF)/NF and UF/RO. Variable removal of N -Nitrosodimethylamine (NDMA) by the RO processes could be attributed to me…
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Removal Characteristics of N-Nitrosamines and Their Precursors by Pilot-Scale Integrated Membrane Systems for Water Reuse
This study investigated the removal characteristics of N -Nitrosamines and their precursors at three pilot-scale water reclamation plants. These plants applies different integrated membrane systems: (1) microfiltration (MF)/nanofiltration (NF)/reverse osmosis (RO) membrane; (2) sand filtration/three-stage RO; and (3) ultrafiltration (UF)/NF and UF/RO. Variable removal of N -Nitrosodimethylamine (NDMA) by the RO processes could be attributed to me…
Mining global soil carbon datasets
The future of terrestrial soil carbon stocks plays a crucial role in climate change prediction. Modern machine learning techniques are now widely applied in soil science to predict the spatial distribution of soil properties from observational data. Beyond prediction, the use of machine learning as a data-mining tool offers a promising pathway for improving soil carbon modelling and refining projections of climate–carbon feedbacks. In this paper,…
Artificial Intelligence (1 work) · Biochemistry (1 work) · Carcinogen (1 work) · Chemistry (1 work) · Chromatography (1 work) · Computer Science (1 work) · Data mining (1 work) · Environmental Chemistry (1 work) · Environmental Science (1 work) · Filtration (mathematics (1 work)