Cascaded Organic Rankine Cycles (ORCs) for Simultaneous Utilization of Liquified Natural Gas (LNG) Cold Energy and Low-Temperature Waste Heat

Liquified Natural Gas (LNG) is a good way to transport natural gas from suppliers to end consumers. LNG contains a huge amount of cold energy due to the energy consumed in the liquefaction process. Generally, the LNG cold energy is lost during the regasification process at the receiving terminal. Power generation with LNG as the heat sink is an energy-efficient and environment-friendly way to regasify LNG. Among different kinds of power generation technologies, Organic Rankine Cycle (ORC) is the most promising power cycle to recover LNG cold energy. ORC has been widely used to convert low-temperature heat into electricity. If low-temperature waste heat and LNG cold energy utilization are utilized simultaneously, the efficiency of the whole system can be improved significantly. However, due to the large temperature difference between the low-temperature waste heat source and LNG, one stage ORC cannot exploit the waste heat and LNG cold energy efficiently. Therefore, a cascaded ORC system is proposed in this study. The optimization of the integrated system is challenging due to the non-convexity and non-linearity of flowsheet and the thermodynamic properties of the working fluids. A simulation-based optimization framework with Particle Swarm Optimization algorithm is adopted to determine the optimal operating conditions of the integrated system. The maximum unit net power output of the integrated system can reach 0.096 kWh per kilogram LNG based on the optimal results.acceptedVersionThis is a post-peer-review, pre-copyedit version of a chapter. Locked until 3 January 2021 due to copyright restrictions. The final authenticated version is available online at: https://doi.org/10.1007/978-981-15-2341-0_5

ATBRG: Adaptive Target-Behavior Relational Graph Network for Effective Recommendation

Recommender system (RS) devotes to predicting user preference to a given item and has been widely deployed in most web-scale applications. Recently, knowledge graph (KG) attracts much attention in RS due to its abundant connective information. Existing methods either explore independent meta-paths for user-item pairs over KG, or employ graph neural network (GNN) on whole KG to produce representations for users and items separately. Despite effectiveness, the former type of methods fails to fully capture structural information implied in KG, while the latter ignores the mutual effect between target user and item during the embedding propagation. In this work, we propose a new framework named Adaptive Target-Behavior Relational Graph network (ATBRG for short) to effectively capture structural relations of target user-item pairs over KG. Specifically, to associate the given target item with user behaviors over KG, we propose the graph connect and graph prune techniques to construct adaptive target-behavior relational graph. To fully distill structural information from the sub-graph connected by rich relations in an end-to-end fashion, we elaborate on the model design of ATBRG, equipped with relation-aware extractor layer and representation activation layer. We perform extensive experiments on both industrial and benchmark datasets. Empirical results show that ATBRG consistently and significantly outperforms state-of-the-art methods. Moreover, ATBRG has also achieved a performance improvement of 5.1% on CTR metric after successful deployment in one popular recommendation scenario of Taobao APP.Comment: Accepted by SIGIR 2020, full paper with 10 pages and 5 figure

MTBRN: Multiplex Target-Behavior Relation Enhanced Network for Click-Through Rate Prediction

Click-through rate (CTR) prediction is a critical task for many industrial systems, such as display advertising and recommender systems. Recently, modeling user behavior sequences attracts much attention and shows great improvements in the CTR field. Existing works mainly exploit attention mechanism based on embedding product when considering relations between user behaviors and target item. However, this methodology lacks of concrete semantics and overlooks the underlying reasons driving a user to click on a target item. In this paper, we propose a new framework named Multiplex Target-Behavior Relation enhanced Network (MTBRN) to leverage multiplex relations between user behaviors and target item to enhance CTR prediction. Multiplex relations consist of meaningful semantics, which can bring a better understanding on users' interests from different perspectives. To explore and model multiplex relations, we propose to incorporate various graphs (e.g., knowledge graph and item-item similarity graph) to construct multiple relational paths between user behaviors and target item. Then Bi-LSTM is applied to encode each path in the path extractor layer. A path fusion network and a path activation network are devised to adaptively aggregate and finally learn the representation of all paths for CTR prediction. Extensive offline and online experiments clearly verify the effectiveness of our framework.Comment: Accepted by CIKM202

A highly pathogenic porcine reproductive and respiratory syndrome virus candidate vaccine based on Japanese encephalitis virus replicon system

In the swine industry, porcine reproductive and respiratory syndrome (PRRS) is a highly contagious disease which causes heavy economic losses worldwide. Effective prevention and disease control is an important issue. In this study, we described the construction of a Japanese encephalitis virus (JEV) DNA-based replicon with a cytomegalovirus (CMV) promoter based on the genome of Japanese encephalitis live vaccine virus SA14-14-2, which is capable of offering a potentially novel way to develop and produce vaccines against a major pathogen of global health. This JEV DNA-based replicon contains a large deletion in the structural genes (C-prM-E). A PRRSV GP5/M was inserted into the deletion position of JEV DNA-based replicons to develop a chimeric replicon vaccine candidate for PRRSV. The results showed that BALB/c mice models with the replicon vaccines pJEV-REP-G-2A-M-IRES and pJEV-REP-G-2A-M stimulated antibody responses and induced a cellular immune response. Analysis of ELSA data showed that vaccination with the replicon vaccine expressing GP5/M induced a better antibodies response than traditional DNA vaccines. Therefore, the results suggested that this ectopic expression system based on JEV DNA-based replicons may represent a useful molecular platform for various biological applications, and the JEV DNA-based replicons expressing GP5/M can be further developed into a novel, safe vaccine candidate for PRRS.</jats:p

Correction to: A Novel Rabbit Model for Benign Biliary Stricture Formation and the Effects of Medication Infusions on Stricture Formation

The original version of the article unfortunately contained an error in funding information. This has been corrected with this erratum. __Funding:__ This research was supported by Applied Basic Research Project of Sichuan Province (2018JY0019)

Effective Treatment of Chronic Proliferative Cholangitis by Local Gentamicin Infusion in Rabbits

Background. Hepatolithiasis is highly prevalent in East Asia characterized by the presence of gallstones in the biliary ducts of the liver. Surgical resection is the potentially curative treatment but bears a high risk of stone recurrence and biliary restenosis. This is closely related to the universal presence of chronic proliferative cholangitis (CPC) in the majority of patients. Recent evidence has indicated the association of bacterial infection with the development of CPC in hepatolithiasis. Thus, this study aims to investigate the feasibility and efficacy of local infusion of gentamicin (an antibiotic) for the treatment of CPC in a rabbit model. Methods. The rabbit CPC model was established based on previously published protocols. Bile duct samples were collected from gentamicin-treated or control animals for pathological and molecular characterization. Results. Histologically, the hyperplasia of biliary epithelium and submucosal glands were inhibited and the thickness of the bile duct wall was significantly decreased after gentamicin therapy. Consistently, the percentage of proliferating cells marked by ki67 was significantly reduced by the treatment. More importantly, this treatment inhibited interleukin 2 production, an essential inflammatory cytokine, and the enzyme activity of endogenous β-Glucuronidase, a key factor in the formation of bile pigment. Conclusions. Local gentamicin infusion effectively inhibits the inflammation, cell proliferation, and lithogenesis in a rabbit model of CPC. This approach represents a potential treatment for CPC and thus prevents recurrent hepatolithiasis