Query suggestion plays an important role in improving the usability of search engines. Although some recently proposed methods can make meaningful query suggestions by mining query patterns from search logs, none of them are context-aware -they do not take into account the immediately preceding queries as context in query suggestion. In this paper, we propose a novel context-aware query suggestion approach which is in two steps. In the offline modellearning step, to address data sparseness, queries are summarized into concepts by clustering a click-through bipartite. Then, from session data a concept sequence suffix tree is constructed as the query suggestion model. In the online query suggestion step, a user's search context is captured by mapping the query sequence submitted by the user to a sequence of concepts. By looking up the context in the concept sequence suffix tree, our approach suggests queries to the user in a context-aware manner. We test our approach on a large-scale search log of a commercial search engine containing 1.8 billion search queries, 2.6 billion clicks, and 840 million query sessions. The experimental results clearly show that our approach outperforms two baseline methods in both coverage and quality of suggestions.

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Weather and climate extremes are critical drivers for deadly and costly natural disasters (IFRC, 2020; UN-DRR, 2020). Understanding their changes and causes have been staying high on the agenda of climate science and risk management sectors (Chen, Moufouma-Okia, et al., 2018). There is rising awareness that the impact of spatially and/or temporally correlated events tends to be disproportionately larger than that of singular hazards as well as the sum of them (Zscheischler et al., 2018). A new paradigm that moves beyond isolated extremes is therefore strongly advocated (Leonard et al., 2014). Compound events were for first time defined in a special report from the Intergovernmental Panel on Climate Change (SREX, Field et al., 2012) as multiple extreme or nonextreme events occurring (1) simultaneously at the same place; (2) concurrently across multiple regions; or (3) in rapid sequence, in the same location. The definition was further refined (Leonard et al., 2014; Zscheischler et al., 2018), with a typology recently proposed (AghaKouchak et al., 2018; Zscheischler et al., 2020). As this field burgeons, a growing body of studies has explored statistical frameworks, observed and projected changes, and attribution of diverse compound events, such as drought-heatwave (Alizadeh et al., 2020; Hao et al., 2013), costal floods combining heavy precipitation, high water level and storm surge (Wahl et al., 2015), and concurrent heatwaves across global breadbaskets

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Two acetylcholinesterase (ace) genes have been reported in many insect species. In pests such as Helicoverpa assulta and Plutella xylostellas, ace1 gene encodes the predominant synaptic enzyme that is the main target of organophosphorus (OP) and carbamate pesticides. It has been reported that pesticide selection has an impact on the ace gene evolution. The domesticated silkworm, Bombyx mori, also has two ace genes. We studied ace gene expression and enzyme activities in silkworm as this has not faced pesticide selection over the past decades. The expression levels of two ace genes, Bm-ace1 and Bm-ace2, were estimated by quantitative real-time polymerase chain reaction. Bm-ace2 was expressed more highly than Bm-ace1 in all tested samples of different developmental stages or tissues, suggesting ace2, rather than ace1, is the major type of acetylcholinesterase (AChE) in Bombyx mori. This is inconsistent with the aforementioned lepidopterons agricultural pests, partly be due to the widespread use of pesticides that may induce high expression of the ace1 gene in these pests. Besides high expression in the head, Bm-ace1 also expresses highly in the silk glands and Bm-ace2 is abundant in the germline, implying both ace genes may have potential non-hydrolytic roles in development. Furthermore, we found that the mRNA levels of two ace genes and their ratios (ace2/ace1) change day to day in the first and third instars. This challenges the conventional method of estimating enzymatic activity using crude extract as an enzyme solution, as it is a mixture of AChE1 and AChE2. An efficient and simple method for separating different AChEs is necessary for reliable toxicological analyses.

Reliable and informative climate projections are fundamental to climate-related risk assessments (King et al., 2015). To create a sense of certainty, the projection information is preferentially presented in the form of externally forced changes (the so-called "signal") along with a "likely range" characterizing un-

A self-standing PPy-based tube common module is obtained by using a reciprocal formwork construction technique. This common module can be further assembled with other building blocks to fabricate high performance linear-shaped supercapacitors.

Query suggestion plays an important role in improving usability of search engines. Although some recently proposed methods provide query suggestions by mining query patterns from search logs, none of them models the immediately preceding queries as context systematically, and uses context information effectively in query suggestions. Context-aware query suggestion is challenging in both modeling context and scaling up query suggestion using context. In this article, we propose a novel context-aware query suggestion approach. To tackle the challenges, our approach consists of two stages. In the first, offline model-learning stage, to address data sparseness, queries are summarized into concepts by clustering a click-through bipartite. A concept sequence suffix tree is then constructed from session data as a context-aware query suggestion model. In the second, online query suggestion stage, a user's search context is captured by mapping the query sequence submitted by the user to a sequence of concepts. By looking up the context in the concept sequence suffix tree, we suggest to the user context-aware queries. We test our approach on large-scale search logs of a commercial search engine containing 4.0 billion Web queries, 5.9 billion clicks, and 1.87 billion search sessions. The experimental results clearly show that our approach outperforms three baseline methods in both coverage and quality of suggestions. H. 2011. Mining concept sequences from largescale search logs for context-aware query suggestion.