基于BERT-BILSTM-CRF模型的电力行业事故文本智能分析*

为解决电力行业事故报告文本较长、语义复杂,难以进行有效文本识别问题,提出1种以BERT作为底层的预训练模型,并设计1种双重注意力机制编码器,结合BILSTM-CRF深度挖掘事故文本语义特征,从而实现文本智能分析。首先构建电力词典,通过对BERT预训练,进行BIO标注,然后引入BILSTM-CRF模型实现对文本标签智能分类,最后将该模型与现行其他4种深度学习模型进行对比。研究结果表明:该模型智能识别精确率、召回率及F1值(查准率)均达到约97%,较其他4种模型中效果最好的模型分别提高0.02,0.03,0.02。研究结果可为电力行业事故报告文本分析提供1种新思路。     

氢化可的松对雄性稀有鮈鲫肾上腺激素调节功能基因的干扰作用

克隆了稀有鮈鲫促肾上腺皮质素释放激素(CRF)、阿片黑素促皮质素原(POMC)和糖皮质素受体(GR)基因片段,设计了物种特异性引物.BLAST分析结果显示,所克隆基因与斑马鱼、鲫鱼和鲤鱼相应基因的同源性超过90%.通过短期暴露实验研究了氢化可的松对稀有鮈鲫雄性成鱼主要脏器的毒性,并通过荧光定量PCR方法测定了其对上述基因表达的影响.结果显示,在0～125μg·L-1浓度暴露下,实验鱼体长、体重、性腺指数、肝脏指数和肾脏指数等指标与对照组均无显著差异.鱼脑CRF基因在暴露组较对照组有显著上调;脑POMC基因在低浓度暴露组显著上调,而在高浓度暴露组显著下调;且CRF和POMC基因的表达谱随浓度变化都呈现非典型剂量效应关系;肝脏GR基因在氢化可的松低暴露浓度组时较对照组明显下调.高浓度组时显著上调.以上结果表明,在分子水平上氢化可的松能够干扰鱼的肾上腺激素调节功能,但在器官和个体水平上没有表现出明显的毒性;HPA轴中的CRF、POMC和GR功能基因可以作为肾上腺皮质激素干扰物的敏感生物标记.     

Multiple indicators, partially ordered sets, and linear extensions: Multi-criterion ranking and prioritization

This paper is concerned with the question of ranking a finite collection of objects when a suite of indicator values is available for each member of the collection. The objects can be represented as a cloud of points in indicator space, but the different indicators (coordinate axes) typically convey different comparative messages and there is no unique way to rank the objects while taking all indicators into account. A conventional solution is to assign a composite numerical score to each object by combining the indicator information in some fashion. Consciously or otherwise, every such composite involves judgments (often arbitrary or controversial) about tradeoffs or substitutability among indicators. Rather than trying to combine indicators, we take the view that the relative positions in indicator space determine only a partial ordering and that a given pair of objects may not be inherently comparable. Working with Hasse diagrams of the partial order, we study the collection of all rankings that are compatible with the partial order (linear extensions). In this way, an interval of possible ranks is assigned to each object. The intervals can be very wide, however. Noting that ranks near the ends of each interval are usually infrequent under linear extensions, a probability distribution is obtained over the interval of possible ranks. This distribution, called the rank-frequency distribution, turns out to be unimodal (in fact, log-concave) and represents the degree of ambiguity involved in attempting to assign a rank to the corresponding object. Stochastic ordering of probability distributions imposes a partial order on the collection of rank-frequency distributions. This collection of distributions is in one-to-one correspondence with the original collection of objects and the induced ordering on these objects is called the cumulative rank-frequency (CRF) ordering; it extends the original partial order. Although the CRF ordering need not be linear, it can be iterated to yield a fixed point of the CRF operator. We hypothesize that the fixed points of the CRF operator are exactly the linear orderings. The CRF operator treats each linear extension as an equal voter in determining the CRF ranking. It is possible to generalize to a weighted CRF operator by giving linear extensions differential weights either on mathematical grounds (e.g., number of jumps) or empirical grounds (e.g., indicator concordance). Explicit enumeration of all possible linear extensions is computationally impractical unless the number of objects is quite small. In such cases, the rank-frequencies can be estimated using discrete Markov chain Monte Carlo (MCMC) methods.