In this paper the score distributions of a number of text search engines are modeled. It is shown empirically that the score distributions on a per query basis may be fitted using an exponential distribution for the set of non-relevant documents and a normal distribution for the set of relevant documents. Experiments show that this model fits TREC-3 and TREC-4 data for not only probabilistic search engines like INQUERY but also vector space search engines like SMART for English. We have also used this model to fit the output of other search engines like LSI search engines and search engines indexing other languages like Chinese.It is then shown that given a query for which relevance information is not available, a mixture model consisting of an exponential and a normal distribution can be fitted to the score distribution. These distributions can be used to map the scores of a search engine to probabilities. We also discuss how the shape of the score distributions arise given certain assumptions about word distributions in documents. We hypothesize that all 'good' text search engines operating on any language have similar characteristics.This model has many possible applications. For example, the outputs of different search engines can be combined by averaging the probabilities (optimal if the search engines are independent) or by using the probabilities to select the best engine for each query. Results show that the technique performs as well as the best current combination techniques.

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Lower Paleozoic sedimentary rocks in the northwestern Tarim basin were strongly altered by complicated geofluids, which resulted in the occurrence of various diagenetic minerals (e.g., dolomite). Here, in situ major, trace, and rare earth element geochemistry of Lower Ordovician diagenetic dolomite grains as well as petrography were performed to unravel the geochemical features, the nature, and origin of the diagenetic fluids. The results indicate that different geochemical information can be detected within a single sample, even within a single dolomite grain. Five generations of diagenetic dolomite have been identified based on geochemical signatures, resulting from four distinct types of diagenetic fluids: (1) HREE enrichment (PAAS-normalized), low RREE, no Eu anomaly, low Mn, Ba, moderate Fe, and high Sr contents are probably due to early burial dolomitizing fluids; (2) MREE enrichment, high RREE, high Mn, Fe, and low Sr content are likely to be associated with Devonian deep-circulating crustal hydrothermal fluids; (3) flat or LREE enrichment pattern with obviously positive Eu anomaly is inferred to be linked to Permian magmatic hydrothermal fluids; and (4) flat REE pattern, moderate RREE, no Eu anomaly, low Mn, Ba, moderate Fe, and high Sr contents are probably due to late burial dolomitizing fluids. The significances of in situ method demonstrated in this study, compared with the whole rock analysis, include not only contamination-free analysis but also unraveling the internal geochemical variation within a single sample or a mineral grain. Thus, for the geochemical study of complicated diagenetic process, in situ method should be preferentially considered.

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With the idea of retrosynthetic analysis, which was raised in the 1960s, chemical synthesis analysis and pathway design have been transformed from a complex problem to a regular process of structural simplification. This review aims to summarize the developments of computer-assisted synthetic analysis and design in recent years, and how machine-learning algorithms contributed to them. LHASA system started the pioneering work of designing semi-empirical reaction modes in computers, with its following rule-based and network-searching work not only expanding the databases, but also building new approaches to indicating reaction rules. Programs like ARChem Route Designer replaced hand-coded reaction modes with automatically-extracted rules, and programs like Chematica changed traditional designing into network searching. Afterward, with the help of machine learning, two-step models which combine reaction rules and statistical methods became the main stream. Recently, fully data-driven learning methods using deep neural networks which even do not require any prior knowledge, were applied into this field. Up to now, however, these methods still cannot replace experienced human organic chemists due to their relatively low accuracies. Future new algorithms with the aid of powerful computational hardware will make this topic promising and with good prospects.

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