Nanoengineering of electrodes via infiltration: an opportunity for developing large-area solid oxide fuel cells with high power density.

Although nanoengineering of electrodes opens up the way to the development of solid oxide fuel cells (SOFCs) with improved performance, the practical implementation of such advances in cells suitable for widespread use remains a challenge. Here, the demonstration of large-area, commercially relevant SOFCs with two nanoengineered electrodes that display excellent performance is reported. The self-assembled nanocomposite La0.6Sr0.4CoO3-δ and Co3O4 is strategically designed and deposited into the well-interconnected Ce0.9Gd0.1O2-δ backbone as a cathode to enable an ultra-large electrochemically active region. The nanometer-scale Ce0.8Gd0.2O2-δ is deposited into a conventional Ni/yttria-stabilized zirconia (YSZ) anode to provide more active oxygen exchange kinetics and electronic conductivity compared to YSZ. The resulting nanoengineered cell with an effective size of 4 cm × 4 cm delivers a remarkable power output of 19.2 W per single cell at 0.6 V and 750 °C. These advancements have potential to facilitate the future development of high-performance SOFCs at a large scale by nanoengineering of electrodes and are expected to pave the way for the commercialization of this technology.

PRESEE: an MDL/MML algorithm to time-series stream segmenting.

Time-series stream is one of the most common data types in data mining field. It is prevalent in fields such as stock market, ecology, and medical care. Segmentation is a key step to accelerate the processing speed of time-series stream mining. Previous algorithms for segmenting mainly focused on the issue of ameliorating precision instead of paying much attention to the efficiency. Moreover, the performance of these algorithms depends heavily on parameters, which are hard for the users to set. In this paper, we propose PRESEE (parameter-free, real-time, and scalable time-series stream segmenting algorithm), which greatly improves the efficiency of time-series stream segmenting. PRESEE is based on both MDL (minimum description length) and MML (minimum message length) methods, which could segment the data automatically. To evaluate the performance of PRESEE, we conduct several experiments on time-series streams of different types and compare it with the state-of-art algorithm. The empirical results show that PRESEE is very efficient for real-time stream datasets by improving segmenting speed nearly ten times. The novelty of this algorithm is further demonstrated by the application of PRESEE in segmenting real-time stream datasets from ChinaFLUX sensor networks data stream.

Automatic symptom name normalization in clinical records of traditional Chinese medicine.

BACKGROUND: In recent years, Data Mining technology has been applied more than ever before in the field of traditional Chinese medicine (TCM) to discover regularities from the experience accumulated in the past thousands of years in China. Electronic medical records (or clinical records) of TCM, containing larger amount of information than well-structured data of prescriptions extracted manually from TCM literature such as information related to medical treatment process, could be an important source for discovering valuable regularities of TCM. However, they are collected by TCM doctors on a day to day basis without the support of authoritative editorial board, and owing to different experience and background of TCM doctors, the same concept might be described in several different terms. Therefore, clinical records of TCM cannot be used directly to Data Mining and Knowledge Discovery. This paper focuses its attention on the phenomena of "one symptom with different names" and investigates a series of metrics for automatically normalizing symptom names in clinical records of TCM. RESULTS: A series of extensive experiments were performed to validate the metrics proposed, and they have shown that the hybrid similarity metrics integrating literal similarity and remedy-based similarity are more accurate than the others which are based on literal similarity or remedy-based similarity alone, and the highest F-Measure (65.62%) of all the metrics is achieved by hybrid similarity metric VSM+TFIDF+SWD. CONCLUSIONS: Automatic symptom name normalization is an essential task for discovering knowledge from clinical data of TCM. The problem is introduced for the first time by this paper. The results have verified that the investigated metrics are reasonable and accurate, and the hybrid similarity metrics are much better than the metrics based on literal similarity or remedy-based similarity alone.