Selection of xerophils and drought tolerant plants is highly crucial in green roof techniques in the drought prone regions of Northwest China. In this study, the thermal performance under the natural conventional climate in summer was analyzed using a self-made simulation experimental platform through comparison of the internal surface temperature with and without green roofs. The distribution frequency of internal surface temperature was investigated by dividing internal surface temperature into several ranges. Statistical analysis showed that the frequency of internal surface temperature lower than 33 • C for green roofs was 91.8%, about 1.09 times higher than that for non-green roofs, and that the sum of internal surface temperature exceeding 35 • C was about one third of that for non-green roofs. The results proved that green roofs have a significant insulation effect. Moreover, the thermal insulation property of green roofs had a strong positive relation with outside temperature. The thermal insulation characteristic was improved as the outdoor temperature increased, additionally, it had a better insulation effect within two hours after irrigation.

20% (w/w) Astragali radix was added to the rice medium to cultivate C. kyushuensis Kob. The fermentation product was collected at mycelium stage, coloring stage, stromata-forming initial stage and fruiting body stage of C. kyushuensis Kob. The dynamic content changes of cordycepin and adenosine were detected at different fermentation stages. In the rice medium with Astragalus radix, both cordycepin and adenosine reached the highest content value on the 30th day of fermentation, 17.31 mg/g and 0.94 mg/g, respectively, which were 8.6 times and 2.0 times of that in rice medium at the same stage. At the same time, transcriptomics technology was used to analyze C. kyushuensis Kob during these four periods.

In this study, the neural network method (Multi-Layer Perceptron, MLP) was integrated with an explorative model, to study the feasibility of using machine learning to reduce the exploration time but providing the same support in long-term water system adaptation planning. The specific network structure and training pattern were determined through a comprehensive statistical trial-and-error (considering the distribution of errors). The network was applied to the case study in Scotchman’s Creek, Melbourne. The network was trained with the first 10% of the exploration data, validated with the following 5% and tested on the rest. The overall root-mean-square-error between the entire observed data and the predicted data is 10.5722, slightly higher than the validation result (9.7961), suggesting that the proposed trial-and-error method is reliable. The designed MLP showed good performance dealing with spatial randomness from decentralized strategies. The adoption of MLP-supported planning may overestimate the performance of candidate urban water systems. By adopting the safety coefficient, a multiplicator or exponent calculated by observed data and predicted data in the validation process, the overestimation problem can be controlled in an acceptable range and have few impacts on final decision making.

Pollutants leaching and control of the extensive green roof (EGR) during rainfall events is crucial to its improvement and promotion, in which the substrate layer plays a key role. Adding...

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