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Abstract The rapidly growing construction industry has accelerated water and energy scarcity in China, threatening its sustainable development. This study integrates multi-regional input-output (MRIO) and data envelopment analysis (DEA) to investigate the water-energy nexus in the construction industry at the provincial level through the entire industrial supply chain. Results show that the construction industry accounts for 8.97% and 27.20% of virtual water and embodied energy in China, respectively. The western area experiences the most energy- and water-intensive construction processes given its backward economy and outdated technological development. The northern area faces great challenges with regard to energy intensity improvements, whereas the central regions suffer from large pressure relating to inefficient water use. The manufacture of non-metallic mineral products, smelting, and the pressing of metals are the largest suppliers of virtual water and embodied energy. The efficiency assessment results demonstrate that Jiangsu and Zhejiang are two DEA-effective regions. China has achieved a relatively high level of scale efficiency but suffers from backward technology.

Abstract Global hydropower growth continues to accelerate with 25% of total capacity installed in just the last 10 years. This accelerating expansion and the important storage facility hydropower means it is increasingly important to understand the reasons for operational failures. This is a challenge because the major reason for failures involves the complex interaction of hydraulic, mechanical and electric subsystems. Historically, reliability modelling has been split in two directions, focusing on different sub-systems, and has not yet been unified. Here these approaches are unified with a novel expression of unbalanced forces. This model with operational data are validated and the important modes of oscillation in the shaft are identified. Finally, the mechanism of the first-order oscillation mode exciting a second-order mode is presented. This integrated and accurate mathematical model is a major advance in the diagnosis and prediction of failures in hydropower operation.

Abstract Plant species that expand their range in response to current climate change will encounter soil communities that may hinder, allow or even facilitate plant performance. It has been shown repeatedly for plant species originating from other continents that these plants are less hampered by soil communities from the new than from the original range. However, information about the interactions between intra‐continental range expanders and soil communities is sparse, especially at community level. Here we used a plant–soil feedback experiment approach to examine if the interactions between range expanders and soil communities change during range expansion. We grew communities of range‐expanding and native plant species with soil communities originating from the original and new range of range expanders. In these conditioned soils, we determined the composition of fungi and bacteria by high‐throughput amplicon sequencing of the ITS region and the 16S rRNA gene respectively. Nematode community composition was determined by microscopy‐based morphological identification. Then we tested how these soil communities influence the growth of subsequent communities of range expanders and natives. We found that after the conditioning phase soil bacterial, fungal and nematode communities differed by origin and by conditioning plant communities. Despite differences in bacterial, fungal and nematode communities between original and new range, soil origin did not influence the biomass production of plant communities. Both native and range expanding plant communities produced most above‐ground biomass in soils that were conditioned by plant communities distantly related to them. Synthesis. Communities of range‐expanding plant species shape specific soil communities in both original and new range soil. Plant–soil interactions of range expanders in communities can be similar to the ones of their closely related native plant species.

In future distribution grids, prosumers (i.e., energy consumers with storage and/or production capabilities) will trade energy with each other and with the main grid. To ensure an efficient and safe operation of energy trading, in this paper, we formulate a peer-to-peer energy market of prosumers as a generalized aggregative game, in which a network operator is only responsible for the operational constraints of the system. We design a distributed market-clearing mechanism with convergence guarantee to an economically-efficient and operationally-safe configuration (i.e., a variational generalized Nash equilibrium). Numerical studies on the IEEE 37-bus testcase show the scalability of the proposed approach and suggest that active participation in the market is beneficial for both prosumers and the network operator. 11 pages, 8 figures. Published in IEEE Transactions on Smart Grid, 2022

The Ganges-Brahmaputra-Meghna (GBM) delta is one of the world's largest deltas. It is currently experiencing high rates of relative sea-level rise of about 5 mm/year, reflecting anthropogenic climate change and land subsidence. This is expected to accelerate further through the 21st Century, so there are concerns that the GBM delta will be progressively submerged. In this context, a core question is: can sedimentation on the delta surface maintain its elevation relative to sea level? This research seeks to answer this question by applying a two-dimensional flow and morphological model which is capable of handling dynamic interactions between the river and floodplain systems and simulating floodplain sedimentation under different flow-sediment regimes and anthropogenic interventions. We find that across a range of flood frequencies and adaptation scenarios (including the natural polder-free state), the retained volume of sediment varies between 22% and 50% of the corresponding sediment input. This translates to average rates of sedimentation on the delta surface of 5.5 mm/yr to 7.5 mm/yr. Hence, under present conditions, sedimentation associated with quasi-natural conditions can exceed current rates of relative sea-level rise and potentially create new land mass. These findings highlight that encouraging quasi-natural conditions through the widespread application of active sediment management measures has the potential to promote more sustainable outcomes for the GBM delta. Practical measures to promote include tidal river management, and appropriate combinations of cross-dams, bandal-like structures, and dredging.

The large size of metabolic networks entails an overwhelming multiplicity in the possible steady-state flux distributions that are compatible with stoichiometric constraints. This space of possibilities is largest in the frequent situation where the nutrients available to the cells are unknown. These two factors: network size and lack of knowledge of nutrient availability, challenge the identification of the actual metabolic state of living cells among the myriad possibilities. Here we address this challenge by developing a method that integrates gene-expression measurements with genome-scale models of metabolism as a means of inferring metabolic states. Our method explores the space of alternative flux distributions that maximize the agreement between gene expression and metabolic fluxes, and thereby identifies reactions that are likely to be active in the culture from which the gene-expression measurements were taken. These active reactions are used to build environment-specific metabolic models and to predict actual metabolic states. We applied our method to model the metabolic states of Saccharomyces cerevisiae growing in rich media supplemented with either glucose or ethanol as the main energy source. The resulting models comprise about 50% of the reactions in the original model, and predict environment-specific essential genes with high sensitivity. By minimizing the sum of fluxes while forcing our predicted active reactions to carry flux, we predicted the metabolic states of these yeast cultures that are in large agreement with what is known about yeast physiology. Most notably, our method predicts the Crabtree effect in yeast cells growing in excess glucose, a long-known phenomenon that could not have been predicted by traditional constraint-based modeling approaches. Our method is of immediate practical relevance for medical and industrial applications, such as the identification of novel drug targets, and the development of biotechnological processes that use complex, largely uncharacterized media, such as biofuel production.