Case study of Net Zero Energy Apartment in Shanghai

A case study regarding an apartment of net zero energy (NZEA) in Shanghai is introduced in this article. The passive design of energy efficient, solar collector system, HVAC&DHW system, indoor terminal units and renewable energy power system of the building are introduced briefly, particularly the concept of the energy system. Based on performance curves obtained from the experiment, a simulation model for the whole system is established for the evaluation. The performance of NZEA was evaluated in terms of the indoor comfort, energy balance and life cycle assessment

Classification of Support Vector Machine and Regression Algorithm

Non

Adversarial-Learned Loss for Domain Adaptation

Recently, remarkable progress has been made in learning transferable representation across domains. Previous works in domain adaptation are majorly based on two techniques: domain-adversarial learning and self-training. However, domain-adversarial learning only aligns feature distributions between domains but does not consider whether the target features are discriminative. On the other hand, self-training utilizes the model predictions to enhance the discrimination of target features, but it is unable to explicitly align domain distributions. In order to combine the strengths of these two methods, we propose a novel method called Adversarial-Learned Loss for Domain Adaptation (ALDA). We first analyze the pseudo-label method, a typical self-training method. Nevertheless, there is a gap between pseudo-labels and the ground truth, which can cause incorrect training. Thus we introduce the confusion matrix, which is learned through an adversarial manner in ALDA, to reduce the gap and align the feature distributions. Finally, a new loss function is auto-constructed from the learned confusion matrix, which serves as the loss for unlabeled target samples. Our ALDA outperforms state-of-the-art approaches in four standard domain adaptation datasets. Our code is available at https://github.com/ZJULearning/ALDA.Comment: Published in 34th AAAI Conference on Artificial Intelligence, 202

Effect of miR-384-targeting LINC00491 on proliferation, migration and invasion of tongue squamous cell carcinoma cells

Purpose: To investigate the effect of long-chain non-coding RNA LINC00491 (LncRNA LINC00491) on the proliferation, migration and invasion of tongue squamous cell carcinoma (TSCC) cells, and the underlying mechanism. Methods: Real-time quantitative polymerase chain reaction (qRT-PCR) was applied to determine the expressions of LINC00491 and micro-RNA-384 (miR-384). Furthermore, LINC00491 and miR-384 were transfected into CAL-27 cells, while cell cycle was analyzed using flow cytometry. Cell proliferation was determined by methyl thiazolyl diphenyl-tetrazolium (MTT) assay. Cell migration and invasion were evaluated using Transwell experiments. The relationship between LINC00491 and miR-384 was confirmed using double luciferase reporting assay, while protein expression levels of P21, Ki67, E- cadherin, N-cadherin, and vimentin were assayed with Western blotting. Results: The expression of LINC00491 increased in TSCC tissues (p < 0.05). The proportion of cells in G1-phase increased, while the proportion of cells in S-phase decreased (p < 0.05). There was decrease in cell survival, cell migration and cell invasion (p < 0.05). The protein expression levels of Ki67, N- cadherin, and vimentin were lowered, while those of P21, E-cadherin protein were increased (p < 0.05). Transfection of LINC00491 and miR- 384 reduced the proportion of cells in G1 phase, but increased the proportion of cells in S-phase (p < 0.05). Moreover, cell survival, migration and invasion were increased. The protein expressions of Ki67, N-cadherin, and vimentin rose, while those of P21 and E-cadherin decreased (p < 0.05). Conclusion: LINC00491 promotes the proliferation, migration and invasion of TSCC cells by inhibiting miR-384. This finding provides a potential target for the treatment of TSCC