FRS: A simple knowledge graph embedding model for entity prediction.

Entity prediction is the task of predicting a missing entity that has a specific relation-ship with another given entity. Researchers usually use knowledge graphs embedding(KGE) methods to embed triples into continuous vectors for computation and perform the tasks of entity prediction. However, KGE models tend to use simple operations to refactor entities and relationships, resulting in insufficient interaction of components of knowledge graphs (KGs), thus limiting the performance of the entity prediction model. In this paper, we propose a new entity prediction model called FRS(Feature Refactoring Scoring) to alleviate the problem of insufficient interaction and solve information incom-pleteness problems in the KGs. Different from the traditional KGE methods of directly using simple operations, the FRS model innovatively provides the procedure of feature processing in the entity prediction tasks, realizing the alignment of entities and relationships in the same feature space and improving the performance of entity prediction model. Although FRS is a simple three-layer network, we find that our own model outperforms state-of-the-art KGC methods in FB15K and WN18. Through extensive experiments on FRS, we discover several insights. For example, the effect of embedding size and negative candidate sampling probability on experimental results is in reverse.

Fast determination of alkylphenol ethoxylates in leafy vegetables using a modified quick, easy, cheap, effective, rugged, and safe method and ultra-high performance supercritical fluid chromatography-tandem mass spectrometry.

In the present study, a quick and sensitive method was developed for simultaneous determination of nonylphenol ethoxylates (NPxEOs) and octylphenol ethoxylates (OPxEOs) (x=2-20) in three leafy vegetables, including cabbage, lettuce, and spinach using a modified "QuEChERS" method and ultra-high performance supercritical fluid chromatography-tandem mass spectrometry (UHPSFC-MS/MS) with scheduled multiple reaction monitoring (MRM). Under optimized conditions, the 38 target analytes were analyzed within a short period of time (5 min). The linearities of the matrix-matched standard calibrations were satisfactory with coefficients of determination (R2)>0.99 and the limits of detection (LOD) and quantification (LOQ) were in between 0.02-0.27 and 0.18-1.75µgkg-1, respectively. The recovery of all target analytes spiked at three (low, medium, and high) fortification levels in various leafy vegetables were ranged from 72.8-122.6% with relative standard deviation (RSD) ≤18.3%. The method was successfully applied to market samples and the target analytes were found in all monitored samples, with total concentrations of 0-8.67µgkg-1 and 15.75-95.75µgkg-1 for OPxEOs and NPxEOs (x=2-20), respectively. In conclusion, the newly developed UHPSFC-ESI-MS/MS method is rapid and versatile and could be extrapolated for qualitative and quantitative analysis of APxEOs in other leafy vegetables.