While the visible plume from a cooling tower is not a pollutant, it can affect the surrounding environment. Moreover, the accompanied evaporation loss has a great potential for wastewater recovery. In the present study, a novel coupling technology for water conservation and plume abatement was proposed, and its feasibility was verified by using thermodynamic analysis. A surface-type heat exchanger was designed and a thermodynamic calculation model was established. Next, based on the principle of “no plume,” the effect of the number of heat exchanger units (N) and the circulating water volume (G) on the water conservation and plume abatement was evaluated under design condition. Results showed that the optimized parameters for the operation of the cooling towers were N = 8 and G < 3000 m3/h, which have a good effect on water conservation and plume abatement. Furthermore, as per the condensation calculation model, the average water conservation amount was 1.105 kg/s and the annual water conservation amount reached 2.8641 × 107 kg.

Mood disorders are ubiquitous mental disorders with familial aggregation. Extracting family history of psychiatric disorders from large electronic hospitalization records is helpful for further study of onset characteristics among patients with a mood disorder. This study uses an observational clinical data set of in-patients of Nanjing Brain Hospital, affiliated with Nanjing Medical University, from the past 10 years. This paper proposes a pretrained language model: Bidirectional Encoder Representations from Transformers (BERT)–Convolutional Neural Network (CNN). We first project the electronic hospitalization records into a low-dimensional dense matrix via the pretrained Chinese BERT model, then feed the dense matrix into the stacked CNN layer to capture high-level features of texts; finally, we use the fully connected layer to extract family history based on high-level features. The accuracy of our BERT–CNN model was 97.12 ± 0.37% in the real-world data set from Nanjing Brain Hospital. We further studied the correlation between mood disorders and family history of psychiatric disorder.

The envelope of rural buildings has been lack of effective and reasonable thermal insulation method and therefore its energy consumption has always been high. In order to address this problem, this paper aims to optimize the thermal design of building envelope. The simulation using DesignBuilder software for modeling and analyzing, using the orthogonal experimental design method to study the effects of external wall, external window and roof on heating load, and optimal thermal insulation scheme was obtained, which was 100mm PUF (external wall), 6mm+12mm+6mm low-E glass (external window) and 100mm PUF (roof). Results revealed that the addition of sunspace can significantly reduce the heating load and thus the selection of window thermal insulation material is very important. Compared with the condition of highest heating load, the energy-efficient rate of optimal scheme reached to 21.4%. The results of this study will serve as the idea for optimal design of rural buildings envelope.