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Hydropower is the largest renewable source of electricity generation. However, the construction and operation of hydropower plants may cause significant material footprints. This study provides a systems accounting framework for evaluating the life-cycle material footprint of a hydropower plant. It is based on the hybrid method as a combination of the process analysis and the input-output analysis. A case study for a typical pumped storage hydropower plant (NPSHP) is carried out to demonstrate the framework. 12 metals (bauxite and aluminum ores, copper ores, gold ores, iron ores, lead ores, nickel ores, PGM ores, silver ores, tin ores, uranium and thorium ores, zinc ores, and other non-ferrous metal ores) and 8 minerals (building stones, chemical and fertilizer minerals, clays and kaolin, gravel and sand, limestone & gypsum & chalk & dolomite, salt, slate, and other minerals) are included. The results can be helpful to promote a sustainable energy transition by incorporating material planetary boundaries into renewable energy systems.

Microbiome-based diagnosis of cancer is an increasingly important supplement for the genomics approach in cancer diagnosis, yet current models for microbiome-based diagnosis of cancer face difficulties in generality: not only diagnosis models could not be adapted from one cancer to another, but models built based on microbes from tissues could not be adapted for diagnosis based on microbes from blood. Therefore, a microbiome-based model suitable for a broad spectrum of cancer types is urgently needed. Here we have introduced DeepMicroCancer, a diagnosis model using artificial intelligence techniques for a broad spectrum of cancer types. Built based on the random forest models it has enabled superior performances on more than twenty types of cancers’ tissue samples. And by using the transfer learning techniques, improved accuracies could be obtained, especially for cancer types with only a few samples, which could satisfy the requirement in clinical scenarios. Moreover, transfer learning techniques have enabled high diagnosis accuracy that could also be achieved for blood samples. These results indicated that certain sets of microbes could, if excavated using advanced artificial techniques, reveal the intricate differences among cancers and healthy individuals. Collectively, DeepMicroCancer has provided a new venue for accurate diagnosis of cancer based on tissue and blood materials, which could potentially be used in clinics.

Hydropower is the largest renewable source of electricity generation, the carbon emissions of which have attracted a lot attention. However, the system boundaries of existing studies are either incomplete or inaccurate. Therefore, this study provides a systems accounting framework for evaluating both the direct and indirect carbon emissions from a hydropower plant. It is based on the hybrid method as a combination of the process analysis and the input-output analysis. To demonstrate the framework, a case study for a typical pumped storage hydropower plant (NPSHP) is carried out. The total carbon emissions are estimated as 5828.39 kt in the life-cycle of the case system. The end-of-use stage causes the largest carbon emissions (38.4%), followed by the construction stage (34.5%), the operation stage (25.6%), and the preparation stage (1.5%). The direct carbon emissions are mainly released from sediments in the end-of-use stage and the surface of reservoirs in the operation stage (94.8%). The indirect carbon emissions are 2.8 times higher than the direct carbon emissions. The material, machinery, energy, and service inputs respectively account for 7.1%, 14.7%, 15.9%, and 62.3% of the total indirect carbon emissions by the case system. The indicator of EGOC (electricity generation on carbon emission) for the NPSHP is calculated as 26.06 g CO2-eq./kWh, which is lower than that of most other power plants.

Previous research papers on urban water resources accounting were confined to the perspectives of production and consumption, ignoring the perspective of income. This paper proposes a systems framework to analyze the income, production, and consumption-based water uses and underlying driving forces of a city based on the methods of multi-scale input-output analysis and structural decomposition analysis. A case study is performed for Shanghai as a megacity. The results show that the income, production and consumption-based water uses of Shanghai had decreased from 5.70 billion m