The Experts below are selected from a list of 20721 Experts worldwide ranked by ideXlab platform
Yijing Nie - One of the best experts on this subject based on the ideXlab platform.
-
strain induced crystallization of natural rubber zinc dimethacrylate composites studied using synchrotron x ray diffraction and molecular simulation
Journal of Polymer Research, 2015Co-Authors: Yijing NieAbstract:Natural rubber (NR) reinforced by in situ polymerization of zinc dimethacrylate (ZDMA) exhibits excellent mechanical properties. However, the corresponding Reinforcement Mechanism is still unclear. Using synchrotron wide-angle X-ray diffraction (WAXD) measurements, we observed that strain-induced crystallization of NR/ZDMA composites had a direct affect on the ultimate mechanical properties. An increase in ZDMA fraction resulted in a lower strain at the onset of crystallization. Further analysis revealed that three factors contributed to the reduction in onset strain, including higher whole cross-linking density due to the emergence of ionic cross-linking clusters, strain amplification of nanodispersion of poly-ZDMA (PZDMA), and the confinement effect of the filler network. The results of dynamic Monte Carlo simulation showed that the confinement effect of the filler network on chain segments favored segmental orientation in regions near the polymer–filler interface, thus inducing a decline in onset strain.
-
new insights into thermodynamic description of strain induced crystallization of peroxide cross linked natural rubber filled with clay by tube model
Polymer, 2011Co-Authors: Yijing Nie, Guangsu Huang, Xiaoan Wang, Gengsheng WengAbstract:Abstract Clay changes the strain-induced crystallization behavior of natural rubber and induces a dual crystallization Mechanism due to the orientation of clay layers during deformation. The structure evolution was probed by in-situ synchrotron wide-angle X-ray diffraction, while the thermodynamics of the onset of crystallization was analyzed by the tube model. The entropy change required for the onset of the strain-induced crystallization of the clay filled rubber is composed of the entropy reduction due to the rubber–filler interactions and also the stretching. The summation of the two kinds of the entropy reduction is nearly equal to that of the neat rubber. The thermodynamic analysis reveals that the orientation of clay layers along the direction of stretching reduces the chain conformational entropy and changes the crystallization Mechanism. These results give some new insights into the strain-induced crystallization process and the Reinforcement Mechanism for the clay filled rubber.
-
improved mechanical properties and special Reinforcement Mechanism of natural rubber reinforced by in situ polymerization of zinc dimethacrylate
Journal of Applied Polymer Science, 2009Co-Authors: Yijing Nie, Guangsu Huang, Peng Zhang, Zhiyuan Liu, Gengsheng WengAbstract:The peroxide-cured natural rubber (NR) was reinforced by in situ polymerization of zinc dimethacrylate (ZDMA). The experimental results showed NR could be greatly reinforced by ZDMA. The tensile strength and the hardness of NR/ZDMA composites increased with the content of ZDMA. The Reinforcement Mechanism was studied further. Both high crosslinking density provided by ionic crosslinking and strain-induced crystallization improved the mechanical properties. The crosslinking density was determined by an equilibrium swelling method and the crystallization index was measured by Wide-angle X-ray diffraction (WXRD). When the amount of ZDMA was high, the ability of strain-induced crystallization decreased, due to the strong interactions between the rubber phase and the hard poly-ZDMA (PZDMA) nanodispersions. At the moment, the increasing ionic crosslinking density made up for the effect of the drop of the strain-induced crystallization, and played a more important role in the Reinforcement. © 2009 Wiley Periodicals, Inc. J Appl Polym Sci, 2010
Azman Hassan - One of the best experts on this subject based on the ideXlab platform.
-
Hibiscus Cannabinus Fiber/PP based Nano-Biocomposites Reinforced with Graphene Nanoplatelets
Journal of Natural Fibers, 2017Co-Authors: Christopher Igwe Idumah, Azman HassanAbstract:The essence of the present research is based on Reinforcement Mechanism of hibiscus cannabinus fiber (KF) and graphene nanoplatelets (GNP) on mechanical and thermal properties of hybrid hibiscus cannabinus fiber/polypropylene (PKMG 1–5 phr) nano-biocomposites. TGA results revealed inclusion of KF and GNP enhanced charring and thermal stability of the bio-nanocomposites. DSC analysis revealed improved melting and crystallization temperatures of the materials. Flexural modulus significantly increased on inclusion of 20 wt.% kenaf fiber and 5 phr GNP. XRD studies confirmed KF and GNP induced nucleation of β-crystals of PP which improved toughness and impact properties of the nano-biocomposites especially at 3 phr.
-
hibiscus cannabinus fiber pp based nano biocomposites reinforced with graphene nanoplatelets
Journal of Natural Fibers, 2017Co-Authors: Christopher Igwe Idumah, Azman HassanAbstract:The essence of the present research is based on Reinforcement Mechanism of hibiscus cannabinus fiber (KF) and graphene nanoplatelets (GNP) on mechanical and thermal properties of hybrid hibiscus cannabinus fiber/polypropylene (PKMG 1–5 phr) nano-biocomposites. TGA results revealed inclusion of KF and GNP enhanced charring and thermal stability of the bio-nanocomposites. DSC analysis revealed improved melting and crystallization temperatures of the materials. Flexural modulus significantly increased on inclusion of 20 wt.% kenaf fiber and 5 phr GNP. XRD studies confirmed KF and GNP induced nucleation of β-crystals of PP which improved toughness and impact properties of the nano-biocomposites especially at 3 phr.
Isam Shahrour - One of the best experts on this subject based on the ideXlab platform.
-
study on rock bolt Reinforcement for a gravity dam foundation
Rock Mechanics and Rock Engineering, 2012Co-Authors: Isam Shahrour, Shenghong Chen, Z M Yang, Weiming WangAbstract:In this article, the rock bolt Reinforcement Mechanism is discussed, and the gravity method as well as the finite element method for the bolted rock is presented. These methods are applied to study the gravity dam foundation of the Xiaoxi Hydropower Project, which is influenced by the presence of a large fault with a cracked zone over 180 m wide. Rock bolt Reinforcement was used to improve the stability of the dam foundation, and the Reinforcement parameters were determined from a semi-empirical equation controlled by in situ test. The stability analysis was conducted using the above methods, and the improvement in terms of deformation and stress as well as stability of the dam foundation due to the Reinforcement is assessed. Rock bolt Reinforcement was completed successfully, and the dam started normal operations in January 2008.
Pingzhong Tang - One of the best experts on this subject based on the ideXlab platform.
-
Reinforcement Mechanism design with applications to dynamic pricing in sponsored search auctions
National Conference on Artificial Intelligence, 2020Co-Authors: Weiran Shen, Binghui Peng, Hanpeng Liu, Michael Zhang, Ruohan Qian, Zhi Guo, Zongyao Ding, Yan Hong, Pingzhong TangAbstract:In many social systems in which individuals and organizations interact with each other, there can be no easy laws to govern the rules of the environment, and agents' payoffs are often influenced by other agents' actions. We examine such a social system in the setting of sponsored search auctions and tackle the search engine's dynamic pricing problem by combining the tools from both Mechanism design and the AI domain. In this setting, the environment not only changes over time, but also behaves strategically. Over repeated interactions with bidders, the search engine can dynamically change the reserve prices and determine the optimal strategy that maximizes the profit. We first train a buyer behavior model, with a real bidding data set from a major search engine, that predicts bids given information disclosed by the search engine and the bidders' performance data from previous rounds. We then formulate the dynamic pricing problem as an MDP and apply a Reinforcement-based algorithm that optimizes reserve prices over time. Experiments demonstrate that our model outperforms static optimization strategies including the ones that are currently in use as well as several other dynamic ones.
-
Reinforcement Mechanism design for fraudulent behaviour in e commerce
National Conference on Artificial Intelligence, 2018Co-Authors: Qingpeng Cai, Pingzhong Tang, Aris Filosratsikas, Yiwei ZhangAbstract:In large e-commerce websites, sellers have been observed to engage in fraudulent behaviour, faking historical transactions in order to receive favourable treatment from the platforms, specifically through the allocation of additional buyer impressions which results in higher revenue for them, but not for the system as a whole. This emergent phenomenon has attracted considerable attention, with previous approaches focusing on trying to detect illicit practices and to punish the miscreants. In this paper, we employ the principles of Reinforcement Mechanism design, a framework that combines the fundamental goals of classical Mechanism design, i.e. the consideration of agents' incentives and their alignment with the objectives of the designer, with deep Reinforcement learning for optimizing the performance based on these incentives. In particular, first we set up a deep-learning framework for predicting the sellers' rationality, based on real data from any allocation algorithm. We use data from one of largest e-commerce platforms worldwide and train a neural network model to predict the extent to which the sellers will engage in fraudulent behaviour. Using this rationality model, we employ an algorithm based on deep Reinforcement learning to optimize the objectives and compare its performance against several natural heuristics, including the platform's implementation and incentive-based Mechanisms from the related literature.
-
Reinforcement Mechanism design for e commerce
The Web Conference, 2018Co-Authors: Qingpeng Cai, Pingzhong Tang, Aris Filosratsikas, Yiwei ZhangAbstract:We study the problem of allocating impressions to sellers in e-commerce websites, such as Amazon, eBay or Taobao, aiming to maximize the total revenue generated by the platform. We employ a general framework of Reinforcement Mechanism design, which uses deep Reinforcement learning to design efficient algorithms, taking the strategic behaviour of the sellers into account. Specifically, we model the impression allocation problem as a Markov decision process, where the states encode the history of impressions, prices, transactions and generated revenue and the actions are the possible impression allocations in each round. To tackle the problem of continuity and high-dimensionality of states and actions, we adopt the ideas of the DDPG algorithm to design an actor-critic policy gradient algorithm which takes advantage of the problem domain in order to achieve convergence and stability. We evaluate our proposed algorithm, coined IA(GRU), by comparing it against DDPG, as well as several natural heuristics, under different rationality models for the sellers - we assume that sellers follow well-known no-regret type strategies which may vary in their degree of sophistication. We find that IA(GRU) outperforms all algorithms in terms of the total revenue.
-
Reinforcement Mechanism design with applications to dynamic pricing in sponsored search auctions
arXiv: Computer Science and Game Theory, 2017Co-Authors: Weiran Shen, Binghui Peng, Michael Zhang, Ruohan Qian, Zongyao Ding, Yan Hong, Pengjun Lu, Pingzhong TangAbstract:In this study, we apply Reinforcement learning techniques and propose what we call Reinforcement Mechanism design to tackle the dynamic pricing problem in sponsored search auctions. In contrast to previous game-theoretical approaches that heavily rely on rationality and common knowledge among the bidders, we take a data-driven approach, and try to learn, over repeated interactions, the set of optimal reserve prices. We implement our approach within the current sponsored search framework of a major search engine: we first train a buyer behavior model, via a real bidding data set, that accurately predicts bids given information that bidders are aware of, including the game parameters disclosed by the search engine, as well as the bidders' KPI data from previous rounds. We then put forward a Reinforcement/MDP (Markov Decision Process) based algorithm that optimizes reserve prices over time, in a GSP-like auction. Our simulations demonstrate that our framework outperforms static optimization strategies including the ones that are currently in use, as well as several other dynamic ones.
-
Reinforcement Mechanism design
International Joint Conference on Artificial Intelligence, 2017Co-Authors: Pingzhong TangAbstract:We put forward a modeling and algorithmic framework to design and optimize Mechanisms in dynamic industrial environments where a designer can make use of the data generated in the process to automatically improve future design. Our solution, coined Reinforcement Mechanism design, is rooted in game theory but incorporates recent AI techniques to get rid of nonrealistic modeling assumptions and to make automated optimization feasible. We instantiate our framework on the key application scenarios of Baidu and Taobao, two of the largest mobile app companies in China. For the Taobao case, our framework automatically designs Mechanisms that allocate buyer impressions for the e-commerce website; for the Baidu case, our framework automatically designs dynamic reserve pricing schemes of advertisement auctions of the search engine. Experiments show that our solutions outperform the state-of-the-art alternatives and those currently deployed, under both scenarios.
Gengsheng Weng - One of the best experts on this subject based on the ideXlab platform.
-
new insights into thermodynamic description of strain induced crystallization of peroxide cross linked natural rubber filled with clay by tube model
Polymer, 2011Co-Authors: Yijing Nie, Guangsu Huang, Xiaoan Wang, Gengsheng WengAbstract:Abstract Clay changes the strain-induced crystallization behavior of natural rubber and induces a dual crystallization Mechanism due to the orientation of clay layers during deformation. The structure evolution was probed by in-situ synchrotron wide-angle X-ray diffraction, while the thermodynamics of the onset of crystallization was analyzed by the tube model. The entropy change required for the onset of the strain-induced crystallization of the clay filled rubber is composed of the entropy reduction due to the rubber–filler interactions and also the stretching. The summation of the two kinds of the entropy reduction is nearly equal to that of the neat rubber. The thermodynamic analysis reveals that the orientation of clay layers along the direction of stretching reduces the chain conformational entropy and changes the crystallization Mechanism. These results give some new insights into the strain-induced crystallization process and the Reinforcement Mechanism for the clay filled rubber.
-
improved mechanical properties and special Reinforcement Mechanism of natural rubber reinforced by in situ polymerization of zinc dimethacrylate
Journal of Applied Polymer Science, 2009Co-Authors: Yijing Nie, Guangsu Huang, Peng Zhang, Zhiyuan Liu, Gengsheng WengAbstract:The peroxide-cured natural rubber (NR) was reinforced by in situ polymerization of zinc dimethacrylate (ZDMA). The experimental results showed NR could be greatly reinforced by ZDMA. The tensile strength and the hardness of NR/ZDMA composites increased with the content of ZDMA. The Reinforcement Mechanism was studied further. Both high crosslinking density provided by ionic crosslinking and strain-induced crystallization improved the mechanical properties. The crosslinking density was determined by an equilibrium swelling method and the crystallization index was measured by Wide-angle X-ray diffraction (WXRD). When the amount of ZDMA was high, the ability of strain-induced crystallization decreased, due to the strong interactions between the rubber phase and the hard poly-ZDMA (PZDMA) nanodispersions. At the moment, the increasing ionic crosslinking density made up for the effect of the drop of the strain-induced crystallization, and played a more important role in the Reinforcement. © 2009 Wiley Periodicals, Inc. J Appl Polym Sci, 2010
