Development of the self-assembled monolayer/MALDI mass spectrometry (SAMDI) platform to enable a high-throughput optimization of a traceless Petasis reaction is described. More than 1800 unique reactions were conducted simultaneously on an array of self-assembled monolayers of alkanethiolates on gold to arrive at optimized conditions, which were then successfully transferred to the solution phase. The utility of this reaction was validated by the efficient synthesis of a variety of di- and trisubstituted allenes.

Global warming has led to significant vegetation changes in recent years. It is necessary to investigate the effects of climatic variations (temperature and precipitation) on vegetation changes for a better understanding of acclimation to climatic change. In this paper, we focused on the integration and application of multi-methods and spatial analysis techniques in GIS to study the spatio-temporal variation of vegetation dynamics and to explore the vegetation change mechanism. The correlations between EVI and climate factors at different time scales were calculated for each pixel including monthly, seasonal and annual scales respectively in Qinghai Lake Basin from the year of 2001 to 2012. The primary objectives of this study are to reveal when, where and why the vegetation change so as to support better understanding of terrestrial response to global change as well as the useful information and techniques for wise regional ecosystem management practices. The main conclusions are as follows: (1) Overall vegetation EVI in the region increased 6% during recent 12 years. The EVI value in growing seasons (i.e. spring and summer) exhibited very significant improving trend, accounted for 12.8% and 9.3% respectively. The spatial pattern of EVI showed obvious spatial heterogeneity which was consistent with hydrothermal condition. In general, the vegetation coverage improved in most parts of the area since nearly 78% pixel of the whole basin showed increasing trend, while degraded slightly in a small part of the area only. (2) The EVI change was positively correlated with average temperature and precipitation. Generally speaking, in Qinghai Lake Basin, precipitation was the dominant driving factor for vegetation growth; however, at different time scale its weight to vegetation has differences. (3) Based on geo-statistical analysis, the autumn precipitation has a strong correlation with the next spring EVI values in the whole region. This findings explore the autumn precipitation is an important indicator, and then, limits the plant growth of next spring.

Hongze Lake and Gaoyou Lake are the source of water for the Grand Canal and the eastern route of the South-to-North Water Transfer (SNWT) project. With the accelerating pace of construction of the SNWT and the initiative to achieve "World Heritage " status for the canal, the water quality and management of the lakes have gained attention in China. Based on analysis of water samples, monitoring data, and the content of heavy metal elements in surface sediments, the water quality of Gaoyou Lake and Hongze Lake have been examined. According to the analysis of the water samples taken in 2003, the content of the heavy metals met the water demands for Hongze Lake and Gaoyou Lake. However, monitoring data of 2004 indicate that the water quality of Hongze Lake and Gaoyou Lake both were worse than grade III which cannot meet the required standard. The heavy metal elements of sediment samples also were above the acceptable environmental standard values. Some projects were launched to promote the lake environment by controlling wastewater emissions, standardizing the types of boats and introducing regulations to protect the lakes. However, problems of vertical and horizontal fragmentation and insufficient public participation in the current management system still exist in the area. Considering the problems confronting the lakes, Integrated Water Resource Management is discussed as an effective approach to overcome the problems.

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Smart Building Technologies hold promise for better livability for residents and lower energy footprints. Yet, the rollout of these technologies, from demand response controls to fault detection and diagnosis, significantly lags behind and is impeded by the current practice of manual identification of sensing point relationships, e.g., how equipment is connected or which sensors are co-located in the same space. This manual process is still error-prone, albeit costly and laborious.We study relation inference among sensor time series. Our key insight is that, as equipment is connected or sensors co-locate in the same physical environment, they are affected by the same real-world events, e.g., a fan turning on or a person entering the room, thus exhibiting correlated changes in their time series data. To this end, we develop a deep metric learning solution that first converts the primitive sensor time series to the frequency domain, and then optimizes a representation of sensors that encodes their relations. Built upon the learned representation, our solution pinpoints the relationships among sensors via solving a combinatorial optimization problem. Extensive experiments on real-world buildings demonstrate the effectiveness of our solution.