Electrospun Poly(Ethylene Oxide) Fibers Reinforced with Poly (Vinylpyrrolidone) Polymer and Cellulose Nanocrystals

Green poly(ethylene oxide) (PEO)/cellulose nanocrystals (CNCs)/poly(vinylpyrrolidone) (PVP) composites were prepared via electrospinning technique. The use of PVP and/or CNCs improved the overall thermal stability and mechanical properties of the PEO fibers. A strong synergistic reinforcing effect was achieved when PVP polymer and CNCs were combined in the composite. This synergistic reinforcement was accompanied with the formation of unique fiber-bead-fiber morphology. The beads were elongated and orientated along the applied force direction during tensile testing, providing an energy dissipation mechanism and a positive reinforcement effect. The combination of CNCs with PVP induced special chemical interactions, and distracted the interactions between PVP and PEO. As a result, the crystallinity of PEO was increased in the system, which also helped enhance fiber properties. The approach developed in this work offers a new way for reinforcing electrospun PEO-based composite fibers for sustainable green composite development

U-rank: Utility-oriented Learning to Rank with Implicit Feedback

Learning to rank with implicit feedback is one of the most important tasks in many real-world information systems where the objective is some specific utility, e.g., clicks and revenue. However, we point out that existing methods based on probabilistic ranking principle do not necessarily achieve the highest utility. To this end, we propose a novel ranking framework called U-rank that directly optimizes the expected utility of the ranking list. With a position-aware deep click-through rate prediction model, we address the attention bias considering both query-level and item-level features. Due to the item-specific attention bias modeling, the optimization for expected utility corresponds to a maximum weight matching on the item-position bipartite graph. We base the optimization of this objective in an efficient Lambdaloss framework, which is supported by both theoretical and empirical analysis. We conduct extensive experiments for both web search and recommender systems over three benchmark datasets and two proprietary datasets, where the performance gain of U-rank over state-of-the-arts is demonstrated. Moreover, our proposed U-rank has been deployed on a large-scale commercial recommender and a large improvement over the production baseline has been observed in an online A/B testing