Solution of steady and unsteady transonic-vortex flows using Euler and full-potential equations

Two methods are presented for inviscid transonic flows: unsteady Euler equations in a rotating frame of reference for transonic-vortex flows and integral solution of full-potential equation with and without embedded Euler domains for transonic airfoil flows. The computational results covered: steady and unsteady conical vortex flows; 3-D steady transonic vortex flow; and transonic airfoil flows. The results are in good agreement with other computational results and experimental data. The rotating frame of reference solution is potentially efficient as compared with the space fixed reference formulation with dynamic gridding. The integral equation solution with embedded Euler domain is computationally efficient and as accurate as the Euler equations

Mechanical and thermal insulation properties of clay-based lightweight concrete

建材生产和住宅采暖将消耗大量能源并排放大量CO2。在混凝土制备中，使用陶土制备具有优异隔热性能的轻质混凝土，并使用煅烧粘土代替部分波特兰水泥，可以大大降低能耗。本研究采用页岩陶土和陶砂分别作为粗骨料和细骨料制备轻质混凝土。首先，确定最佳体积砂比。然后，使用煅烧粘土和石灰石的剂量从30%，50%到70%。研究了煅烧粘土和石灰石含量对混凝土微观结构、力学性能和隔热性能的影响。结果表明，最佳体积砂比为50%。当煅烧粘土和石灰石的用量为30%时，强度在所有年龄段最高，28 d时强度可达33.6 MPa，比对照组高12%。当更换水平不断增加时，所有年龄段的实力开始下降。所制备的粘土基轻质混凝土的导热系数低至0.144 W/mK，表现出优异的保温性能。本研究制备的轻质混凝土具有较高的强度和优异的保温性能，具有良好的应用前景

Gravity Effects on Information Filtering and Network Evolving

In this paper, based on the gravity principle of classical physics, we propose a tunable gravity-based model, which considers tag usage pattern to weigh both the mass and distance of network nodes. We then apply this model in solving the problems of information filtering and network evolving. Experimental results on two real-world data sets, \emph{Del.icio.us} and \emph{MovieLens}, show that it can not only enhance the algorithmic performance, but can also better characterize the properties of real networks. This work may shed some light on the in-depth understanding of the effect of gravity model

Promoting cold-start items in recommender systems

As one of major challenges, cold-start problem plagues nearly all recommender systems. In particular, new items will be overlooked, impeding the development of new products online. Given limited resources, how to utilize the knowledge of recommender systems and design efficient marketing strategy for new items is extremely important. In this paper, we convert this ticklish issue into a clear mathematical problem based on a bipartite network representation. Under the most widely used algorithm in real e-commerce recommender systems, so-called the item-based collaborative filtering, we show that to simply push new items to active users is not a good strategy. To our surprise, experiments on real recommender systems indicate that to connect new items with some less active users will statistically yield better performance, namely these new items will have more chance to appear in other users' recommendation lists. Further analysis suggests that the disassortative nature of recommender systems contributes to such observation. In a word, getting in-depth understanding on recommender systems could pave the way for the owners to popularize their cold-start products with low costs.Comment: 6 pages, 6 figure