Indoor overheating in high window-wall ratio (WWR) buildings has drawn widespread attention, but there is limited research on it during winter. Similarly, the application of radiative cooling (RC) ...technology on high-WWR facades and the effectiveness of combining RC with phase change materials (PCMs) need exploration. In Guangzhou, typical rooms in commercial buildings were studied, revealing that high-WWR buildings experience greater indoor overheating in winter compared to summer. For a 100 % WWR, the total equivalent energy consumption (EEC) is 71.389 kWh·m−2 in winters, 7.8 % higher than summer. Using RC glass with high solar (diffuse) reflectivity (0.65) on south-facing windows reduced EEC by over 70 %. RC glass also provided significant cooling capacity during unoccupied periods, which is sufficient to meet daytime cooling needs. In the case of a 100 % WWR, the proportion of available cooling capacity (37.5 %) during unoccupied period exceeded daytime cooling EEC (24.9 %). Therefore, PCMs were adopted to store this cooling capacity and transfer it for release during the occupied period, which bring an additional improvement in energy-saving performance by 4.7 %. The combination of PCMs and RC technology further achieves building energy efficiency. This study offers insights for addressing winter indoor overheating in high-WWR buildings.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Graph representation learning aims to embed each vertex in a graph into a low-dimensional vector space. Existing graph representation learning methods can be classified into two categories: ...generative models that learn the underlying connectivity distribution in a graph, and discriminative models that predict the probability of edge between a pair of vertices. In this paper, we propose GraphGAN , an innovative graph representation learning framework unifying the above two classes of methods, in which the generative and the discriminative model play a game-theoretical minimax game. Specifically, for a given vertex, the generative model tries to fit its underlying true connectivity distribution over all other vertices and produces "fake" samples to fool the discriminative model, while the discriminative model tries to detect whether the sampled vertex is from ground truth or generated by the generative model. With the competition between these two models, both of them can alternately and iteratively boost their performance. Moreover, we propose a novel graph softmax as the implementation of the generative model to overcome the limitations of traditional softmax function, which can be proven satisfying desirable properties of normalization , graph structure awareness , and computational efficiency . Through extensive experiments on real-world datasets, we demonstrate that GraphGAN achieves substantial gains in a variety of applications, including graph reconstruction, link prediction, node classification, recommendation, and visualization, over state-of-the-art baselines.
By means of density functional theory calculations, we successfully predict two stable 2D triangular borophenes, namely B3H and B6O. Our results indicate that B3H is a Dirac material and its cone ...point is located at the K point of the Brillouin zone (BZ). B6O is identified as having a node-line ring and Dirac cones together. Its node-line ring formed by the intersection of the extended energy band from the two Dirac cones located on K point. This modified 2D borophene has great thermal and dynamic stability due to the electron transfer from the triangular boron lattice to the O atoms. The electronic structure of B6O nanofilm demonstrates novel properties such as two Dirac cones, more than 1.3 eV linear dispersion bands at some points of the BZ, as well as excellent transport properties for the extremely high mobility brought by the combination of the node-line semimetal and Dirac cones. Our study may motivate potential applications of 2D materials in nanoelectronics.
Water disinfection and food pasteurization are critical to reducing waterborne and foodborne diseases, which have been a pressing public health issue globally. Electrified treatment processes are ...emerging and have become promising alternatives due to the low cost of electricity, independence of chemicals, and low potential to form by-products. Electric field treatment (EFT) is a physical pathogen inactivation approach, which damages cell membrane by irreversible electroporation. EFT has been studied for both water disinfection and food pasteurization. However, no study has systematically connected the two fields with an up-to-date review. In this article, we first provide a comprehensive background of microbial control in water and food, followed by the introduction of EFT. Subsequently, we summarize the recent EFT studies for pathogen inactivation from three aspects, the processing parameters, its efficacy against different pathogens, and the impact of liquid properties on the inactivation performance. We also review the development of novel configurations and materials for EFT devices to address the current challenges of EFT. This review introduces EFT from an engineering perspective and may serve as a bridge to connect the field of environmental engineering and food science.
Display omitted
•The similarities and differences of EFT in water and food are compared.•Latest advances of the impact of operation parameters and liquid properties are reviewed.•EFT on bacteria, viruses, and protozoa are systematically reviewed.•Three future directions to promote the applications of EFT are proposed.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Nowadays, thermal conductivity as a key parameter affecting the thermal performance of PCM has attracted much attention. However, the thermal performance evaluation of ultra-high thermal conductivity ...PCM is scarce, especially in field of building energy conservation. Therefore, we traversed the range of thermal conductivity that can be prepared at present and within the expected time frame, which is 0.08–49.28 W⋅m−1K−1. In addition, a novel index, Utilization Rate of Latent Heat (URLH), was proposed to evaluate matching degree between PCM and a certain application background in buildings, so as to explore the reason of poor energy-saving effect of PCM under some application backgrounds. We found that the thermal conductivity of PCM is not the higher the better in buildings, in other words, a turning point appears. The optimal thermal conductivity is about 0.6 W⋅m−1K−1 and when the value is lower than 0.3 W⋅m−1K−1, URLH is equal to 0 in the cases of Shanghai. Therefore, we recommend PCM researchers to evaluate the URLH before using PCM to avoid the ineffective use of materials. Through the analysis of URLH, researchers can know the specific parameters that affect the thermal performance of PCM and obtain the way to optimize them.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Abstract
The purpose of this study is to provide new empirical evidence on the coupling relation between extraction of mineral resources and the ecological environment in the area of Inner Mongolia ...grassland and come up with countermeasures in favor of their balanced development. To this end, the study designs a comprehensive indicator framework and creates a coupling model based on the system theory and Lyapunov’s approximation theorem. The study gauges the coupling degree concerning mineral resources exploitation and the ecological environment over the period from 2002 to 2018, finding that the stress, which mineral resources exploitation exercises on the ecological environment in the grassland area of Inner Mongolian, is constantly intensifying. The study also finds that their coupling relationship turns out to be at a low level of coordination, even representing a downward trend. Albeit this region moves into the stage of harmonious development between mining activity and environmental protection, their coupling degree is tiny. This paper concludes with pointing out proposals on how to promote the balanced development of mineral resources exploitation and the ecological environment.
Recommendation has provoked vast amount of attention and research in recent decades. Most previous works employ matrix factorization techniques to learn the latent factors of users and items. And ...many subsequent works consider external information, e.g., social relationships of users and items’ attributions, to improve the recommendation performance under the matrix factorization framework. However, matrix factorization methods may not make full use of the limited information from rating or check-in matrices, and achieve unsatisfying results. Recently, deep learning has proven able to learn good representation in natural language processing, image classification, and so on. Along this line, we propose a new representation learning framework called Recommendation via Dual-Autoencoder (ReDa). In this framework, we simultaneously learn the new hidden representations of users and items using autoencoders, and minimize the deviations of training data by the learnt representations of users and items. Based on this framework, we develop a gradient descent method to learn hidden representations. Extensive experiments conducted on several real-world data sets demonstrate the effectiveness of our proposed method compared with state-of-the-art matrix factorization based methods.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
•We summarize the significant contributions of the proposed method as follows:.•The proposed method uses the moving average filter (MAF)-quadrature signal generator (QSG) to filter out high-order ...harmonics and extract the features from the fault voltage.•The proposed method utilizes fundamental and nearby frequency components to identify fault types and determine the arc extinction time if the fault is transient.•The proposed method is easy to implement without communication and has a small computation.
An adaptive single-phase auto-reclosing method based on the moving average filter (MAF)-quadrature signal generator (QSG) is developed to determine secondary arc extinction time for high-voltage transmission lines with shunt reactors. Firstly, the difference between permanent and transient faults is derived and analyzed by the equivalent circuit model. Then, MAF-QSG is utilized to extract these features from the fault voltage. During the second arc period, for transient fault, the fundamental component and subharmonic component with around 50 Hz are reserved by MAF-QSG; however, in the case of permanent fault, only the fundamental component is retained. Finally, the identification index and its threshold are designed above the extraction fault features. The performance of the proposed method is evaluated by simulation and field data studies, accounting for different transmission line compensation levels, transition resistances, and fault location conditions.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
499.
Potential Friend Recommendation in Online Social Network Xing Xie
2010 IEEE/ACM Int'l Conference on Green Computing and Communications & Int'l Conference on Cyber, Physical and Social Computing,
2010-Dec.
Conference Proceeding
As the wide popularization of online social networks, online users are not content only with keeping online friendship with social friends in real life any more. They hope the system designers can ...help them exploring new friends with common interest. However, the large amount of online users and their diverse and dynamic interests possess great challenges to support such a novel feature in online social networks. In this paper, by leveraging interest-based features, we design a general friend recommendation framework, which can characterize user interest in two dimensions: context (location, time) and content, as well as combining domain knowledge to improve recommending quality. We also design a potential friend recommender system in a real online social network of biology field to show the effectiveness of our proposed framework.
Wide peritoneal metastasis is the cause of the highest lethality of ovarian cancer in gynecologic malignancies. Ascites play a key role in ovarian cancer metastasis, but involved mechanism is ...uncertain. Here, we performed a quantitative proteomics of ascites, and found that collagen type I alpha 1 (COL1A1) was notably elevated in ascites from epithelial ovarian cancer patients compared to normal peritoneal fluids, and verified that elevated COL1A1 was mainly originated from fibroblasts. COL1A1 promoted migration and invasion of ovarian cancer cells, but such effects were partially eliminated by COL1A1 antibodies. Intraperitoneally injected COL1A1 accelerated intraperitoneal metastasis of ovarian cancer xenograft in NOD/SCID mice. Further, COL1A1 activated downstream AKT phosphorylation by binding to membrane surface receptor integrin β1 (ITGB1). Knockdown or blockage of ITGB1 reversed COL1A1 enhanced migration and invasion in ovarian cancer cells. Conversely, ovarian cancer ascites and fibrinogen promoted fibroblasts to secrete COL1A1. Elevated fibrinogen in ascites might be associated with increased vascular permeability induced by ovarian cancer. Our findings suggest that microenvironment remodeled by tumor cells and stromal cells promotes fibroblasts to secrete COL1A1 and facilitates the metastasis of ovarian cancer, which may provide a new approach for ovarian cancer therapeutics.
•COL1A1 protein is elevated in ovarian cancer ascites and derived from fibroblasts.•COL1A1 promotes ovarian cancer metastasis via activating ITGB1/AKT signal pathway.•Ovarian cancer ascites and fibrinogen promote fibroblasts to secrete COL1A1.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
PDF
