Improving the indoor thermal environment with ceiling radiant terminals

A CFD (computational Fluid Dynamics) simulation model of the porous ceiling radiant air-conditioning system was established to study the influence of the ceiling temperature and envelope temperature (including the temperature of the walls and the floor of a room) on the thermal environment in the room equipped with such a system. The results showed that, for the summer condition, higher ceiling temperatures would result in higher indoor air temperature and higher Predicted Percentage Dissatisfied (PPD), which meant potential discomfort of occupants in the room. For the winter condition, however, a higher ceiling temperature within 28°C would result in a lower PPD, thus improved the thermal comfort. Considering the energy-conservation, the thermal comfort could be assured if the ceiling temperature was not more than 28°C. As for the effect of envelope temperature, the result showed that the increase in the envelope temperature during summer could result in a higher indoor air temperature, but the thermal comfort of occupants could still be ensured under such condition. Considering both the thermal comfort and the energyconservation, a ceiling temperature of 18°C (underside surface temperature of the ceiling) and an envelope temperature between 26°C and 32°C were proved appropriate for the summer. Similarly, based on the simulation results, a ceiling temperature of 26°C, and an envelope temperature between 8°C and 11°C were found appropriate for the winter. The results indicated that for the porous ceiling radiant air-conditioning system, ceiling temperature should be controlled to increase the ratio of radiant heat transfer in the summer, and the envelope temperature should be lowered to improve the energy-conservation of the system. In the winter, the heat transfer by radiation of the porous ceiling would account for a larger ratio, therefore the system showed good heating capacity and energyconservation performance in winter.publishedVersio

Improving Multi-Person Pose Tracking with A Confidence Network

Human pose estimation and tracking are fundamental tasks for understanding human behaviors in videos. Existing top-down framework-based methods usually perform three-stage tasks: human detection, pose estimation and tracking. Although promising results have been achieved, these methods rely heavily on high-performance detectors and may fail to track persons who are occluded or miss-detected. To overcome these problems, in this paper, we develop a novel keypoint confidence network and a tracking pipeline to improve human detection and pose estimation in top-down approaches. Specifically, the keypoint confidence network is designed to determine whether each keypoint is occluded, and it is incorporated into the pose estimation module. In the tracking pipeline, we propose the Bbox-revision module to reduce missing detection and the ID-retrieve module to correct lost trajectories, improving the performance of the detection stage. Experimental results show that our approach is universal in human detection and pose estimation, achieving state-of-the-art performance on both PoseTrack 2017 and 2018 datasets.Comment: Accepted by IEEE Transactions on Multimedia. 11 pages, 5 figure

Growth and applications of two-dimensional single crystals

Two-dimensional (2D) materials have received extensive research attentions over the past two decades due to their intriguing physical properties (such as the ultrahigh mobility and strong light-matter interaction at atomic thickness) and a broad range of potential applications (especially in the fields of electronics and optoelectronics). The growth of single-crystal 2D materials is the prerequisite to realize 2D-based high-performance applications. In this review, we aim to provide an in-depth analysis of the state-of-the-art technology for the growth and applications of 2D materials, with particular emphasis on single crystals. We first summarize the major growth strategies for monolayer 2D single crystals. Following that, we discuss the growth of multilayer single crystals, including the control of thickness, stacking sequence, and heterostructure composition. Then we highlight the exploration of 2D single crystals in electronic and optoelectronic devices. Finally, a perspective is given to outline the research opportunities and the remaining challenges in this field

Genetic immunization with Hantavirus vaccine combining expression of G2 glycoprotein and fused interleukin-2

In this research, we developed a novel chimeric HTNV-IL-2-G2 DNA vaccine plasmid by genetically linking IL-2 gene to the G2 segment DNA and tested whether it could be a candidate vaccine. Chimeric gene was first expressed in eukaryotic expression system pcDNA3.1 (+). The HTNV-IL-2-G2 expressed a 72 kDa fusion protein in COS-7 cells. Meanwhile, the fusion protein kept the activity of its parental proteins. Furthermore, BALB/c mice were vaccinated by the chimeric gene. ELISA, cell microculture neutralization test in vitro were used to detect the humoral immune response in immunized BALB/c mice. Lymphocyte proliferation assay was used to detect the cellular immune response.- The results showed that the chimeric gene could simultaneously evoke specific antibody against G2 glycoprotein and IL-2. And the immunized mice of every group elicited neutralizing antibodies with different titers. Lymphocyte proliferation assay results showed that the stimulation indexes of splenocytes of chimeric gene to G2 and IL-2 were significantly higher than that of other groups. Our results suggest that IL-2-based HTNV G2 DNA can induce both humoral and cellular immune response specific for HTNV G2 and can be a candidate DNA vaccine for HTNV infection

Category-Specific CNN for Visual-aware CTR Prediction at JD.com

As one of the largest B2C e-commerce platforms in China, JD com also powers a leading advertising system, serving millions of advertisers with fingertip connection to hundreds of millions of customers. In our system, as well as most e-commerce scenarios, ads are displayed with images.This makes visual-aware Click Through Rate (CTR) prediction of crucial importance to both business effectiveness and user experience. Existing algorithms usually extract visual features using off-the-shelf Convolutional Neural Networks (CNNs) and late fuse the visual and non-visual features for the finally predicted CTR. Despite being extensively studied, this field still face two key challenges. First, although encouraging progress has been made in offline studies, applying CNNs in real systems remains non-trivial, due to the strict requirements for efficient end-to-end training and low-latency online serving. Second, the off-the-shelf CNNs and late fusion architectures are suboptimal. Specifically, off-the-shelf CNNs were designed for classification thus never take categories as input features. While in e-commerce, categories are precisely labeled and contain abundant visual priors that will help the visual modeling. Unaware of the ad category, these CNNs may extract some unnecessary category-unrelated features, wasting CNN's limited expression ability. To overcome the two challenges, we propose Category-specific CNN (CSCNN) specially for CTR prediction. CSCNN early incorporates the category knowledge with a light-weighted attention-module on each convolutional layer. This enables CSCNN to extract expressive category-specific visual patterns that benefit the CTR prediction. Offline experiments on benchmark and a 10 billion scale real production dataset from JD, together with an Online A/B test show that CSCNN outperforms all compared state-of-the-art algorithms

Pre-Operative Prediction of Mediastinal Node Metastasis Using Radiomics Model Based on 18F-FDG PET/CT of the Primary Tumor in Non-Small Cell Lung Cancer Patients

Purpose: We investigated whether a fluorine-18-fluorodeoxy glucose positron emission tomography/computed tomography (18F-FDG PET/CT)-based radiomics model (RM) could predict the pathological mediastinal lymph node staging (pN staging) in patients with non-small cell lung cancer (NSCLC) undergoing surgery.Methods: A total of 716 patients with a clinicopathological diagnosis of NSCLC were included in this retrospective study. The prediction model was developed in a training cohort that consisted of 501 patients. Radiomics features were extracted from the 18F-FDG PET/CT of the primary tumor. Support vector machine and extremely randomized trees were used to build the RM. Internal validation was assessed. An independent testing cohort contained the remaining 215 patients. The performances of the RM and clinical node staging (cN staging) in predicting pN staging (pN0 vs. pN1 and N2) were compared for each cohort. The area under the curve (AUC) of the receiver operating characteristic curve was applied to assess the model's performance.Results: The AUC of the RM [0.81 (95% CI, 0.771–0.848); sensitivity: 0.794; specificity: 0.704] for the predictive performance of pN1 and N2 was significantly better than that of cN in the training cohort [0.685 (95% CI, 0.644–0.728); sensitivity: 0.804; specificity: 0.568], (P-value = 8.29e-07, as assessed by the Delong test). In the testing cohort, the AUC of the RM [0.766 (95% CI, 0.702–0.830); sensitivity: 0.688; specificity: 0.704] was also significantly higher than that of cN [0.685 (95% CI, 0.619–0.747); sensitivity: 0.799; specificity: 0.568], (P = 0.0371, Delong test).Conclusions: The RM based on 18F-FDG PET/CT has a potential for the pN staging in patients with NSCLC, suggesting that therapeutic planning could be tailored according to the predictions