Learning transportation mode from raw gps data for geographic applications on the web

Geographic information has spawned many novel Web applications where global positioning system (GPS) plays important roles in bridging the applications and end users. Learning knowledge from users ’ raw GPS data can provide rich context information for both geographic and mobile applications. However, so far, raw GPS data are still used directly without much understanding. In this paper, an approach based on supervised learning is proposed to automatically infer transportation mode from raw GPS data. The transportation mode, such as walking, driving, etc., implied in a user’s GPS data can provide us valuable knowledge to understand the user. It also enables context-aware computing based on user’s present transportation mode and design of an innovative user interface for Web users. Our approach consists of three parts: a change point

Effect of Heterogeneous Microstructure on Refining Austenite Grain Size in Low Alloy Heavy-Gage Plate

The present work introduces the role of heterogeneous microstructure in enhancing the nucleation density of reversed austenite. It was found that the novel pre-annealing produced a heterogeneous microstructure consisting of alloying elements-enriched martensite and alloying-depleted intercritical ferrite. The shape of the martensite at the prior austenite grain boundary was equiaxed and acicular at inter-laths. The equiaxed reversed austenite had a K-S orientation with adjacent prior austenite grain, and effectively refined the prior austenite grain that it grew into. The alloying elements-enriched martensite provided additional nucleation sites to form equiaxed reversed austenite at both prior austenite grain boundaries and intragranular inter-lath boundaries during re-austenitization. It was revealed that prior austenite grain size was refined to ~12 μm by pre-annealing and quenching, while it was ~30 μm by conventional quenching. This is a practical way of refining transformation products by refining prior austenite grain size to improve the strength, ductility and low temperature toughness of heavy-gage plate steel

Probing the Role of Pore Architecture of Carbon Support in the Stability of Iron Phthalocyanine during Oxygen Reduction

Emerging carbon-based molecular catalysts with a single metal active center possess attractive oxygen electroreduction performance comparable with that of commercial Pt/C catalysts. Nonetheless, the relative instability curtails their widespread industrial application. Research has started to clarify the mechanisms behind the degradation of the active site itself. However, the impact of the carbon support on the catalyst stability remains not fully understood. Here, we employed carbon supports with distinct pore structures (e.g., Ketjen black, carbon nanotube) to load iron phthalocyanine (FePc), which serves as a model single metal active center. The resulting catalysts exhibited markedly divergent stability with current density decreases of 63% and 34% over 10 h of amperometric I–t test, respectively. By integrating in situ electrochemical impedance spectroscopy (EIS) with distribution of relaxation times (DRT) analysis to dissect degradation pathways, we have found that variations in pore structures decisively impact the wetting behavior and mass transfer efficiency within the microenvironment around the catalytic sites, thus greatly influencing stability. Our insights provide a new viewpoint and strategic approach for designing carbon-based catalysts with highly a stable single metal active site