Formation of Ti–Zr–Cu–Ni bulk metallic glasses

Formation of bulk metallic glass in quaternary Ti–Zr–Cu–Ni alloys by relatively slow cooling from the melt is reported. Thick strips of metallic glass were obtained by the method of metal mold casting. The glass forming ability of the quaternary alloys exceeds that of binary or ternary alloys containing the same elements due to the complexity of the system. The best glass forming alloys such as Ti34Zr11Cu47Ni8 can be cast to at least 4-mm-thick amorphous strips. The critical cooling rate for glass formation is of the order of 250 K/s or less, at least two orders of magnitude lower than that of the best ternary alloys. The glass transition, crystallization, and melting behavior of the alloys were studied by differential scanning calorimetry. The amorphous alloys exhibit a significant undercooled liquid region between the glass transition and first crystallization event. The glass forming ability of these alloys, as determined by the critical cooling rate, exceeds what is expected based on the reduced glass transition temperature. It is also found that the glass forming ability for alloys of similar reduced glass transition temperature can differ by two orders of magnitude as defined by critical cooling rates. The origins of the difference in glass forming ability of the alloys are discussed. It is found that when large composition redistribution accompanies crystallization, glass formation is enhanced. The excellent glass forming ability of alloys such as Ti34Zr11Cu47Ni8 is a result of simultaneously minimizing the nucleation rate of the competing crystalline phases. The ternary/quaternary Laves phase (MgZn2 type) shows the greatest ease of nucleation and plays a key role in determining the optimum compositions for glass formation

A Probabilistic Embedding Clustering Method for Urban Structure Detection

Urban structure detection is a basic task in urban geography. Clustering is a core technology to detect the patterns of urban spatial structure, urban functional region, and so on. In big data era, diverse urban sensing datasets recording information like human behaviour and human social activity, suffer from complexity in high dimension and high noise. And unfortunately, the state-of-the-art clustering methods does not handle the problem with high dimension and high noise issues concurrently. In this paper, a probabilistic embedding clustering method is proposed. Firstly, we come up with a Probabilistic Embedding Model (PEM) to find latent features from high dimensional urban sensing data by learning via probabilistic model. By latent features, we could catch essential features hidden in high dimensional data known as patterns; with the probabilistic model, we can also reduce uncertainty caused by high noise. Secondly, through tuning the parameters, our model could discover two kinds of urban structure, the homophily and structural equivalence, which means communities with intensive interaction or in the same roles in urban structure. We evaluated the performance of our model by conducting experiments on real-world data and experiments with real data in Shanghai (China) proved that our method could discover two kinds of urban structure, the homophily and structural equivalence, which means clustering community with intensive interaction or under the same roles in urban space.Comment: 6 pages, 7 figures, ICSDM201

Remark on approximation in the calculation of the primordial spectrum generated during inflation

We re-examine approximations in the analytical calculation of the primordial spectrum of cosmological perturbation produced during inflation. Taking two inflation models (chaotic inflation and natural inflation) as examples, we numerically verify the accuracy of these approximations.Comment: 10 pages, 6 figures, to appear in PR