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A mass balance method established in this laboratory was applied to determine the purity of an endosulfan-II pure substance. Gas chromatography-flame ionization detector (GC-FID) was used to measure organic impurities. Total of 10 structurally related organic impurities were detected by GC-FID in the material. Water content was determined to be 0.187% by Karl-Fischer (K-F) coulometry with an oven-drying method. Nonvolatile residual impurities was not detected by Thermal gravimetric analysis (TGA) within the detection limit of 0.04% (0.7 µg in absolute amount). Residual solvents within the substance were determined to be 0.007% in the Endosulfan-II pure substance by running GC-FID after dissolving it with two solvents. The purity of the endosulfan-II was finally assigned to be (99.17 ± 0.14)%. Details of the mass balance method including interpretation and evaluating uncertainties of results from each individual methods and the finally assayed purity were also described.

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The quantitative analysis of digitized historical documents has begun in earnest in recent years. Text classification is of particular importance for quantitative historical analysis because it helps to search literature efficiently and to determine the important subjects of a particular age. While numerous historians have joined together to classify large-scale historical documents, consistent classification among individual researchers has not been achieved. In this study, we present a classification method for large-scale historical data that uses a recently developed supervised learning algorithm called the Hierarchical Attention Network (HAN). By applying various classification methods to the Annals of the Joseon Dynasty (AJD), we show that HAN is more accurate than conventional techniques with word-frequency-based features. HAN provides the extent that a particular sentence or word contributes to the classification process through a quantitative value called 'attention'. We extract the representative keywords from various categories by using the attention mechanism and show the evolution of the keywords over the 472-year span of the AJD. Our results reveal that largely two groups of event categories are found in the AJD. In one group, the representative keywords of the categories were stable over long periods while the keywords in the other group varied rapidly, exhibiting repeatedly changing characteristics of the categories. Observing such macroscopic changes of representative words may provide insight into how a particular topic changes over a historical period.

While huge strides have recently been made in language-based machine learning, the ability of artificial systems to comprehend the sequences that comprise animal behavior has been lagging behind. In contrast, humans instinctively recognize behaviors by finding similarities in behavioral sequences. Here, we develop an unsupervised behavior-mapping framework, SUBTLE (spectrogram-UMAP-based temporal-link embedding), to capture comparable behavioral repertoires from 3D action skeletons. To find the best embedding method, we devise a temporal proximity index as a metric to gauge temporal representation in the behavioral embedding space. The method achieves the best performance compared to current embedding strategies. Its spectrogram-based UMAP clustering not only identifies subtle inter-group differences but also matches human-annotated labels. SUBTLE framework automates the tasks of both identifying behavioral repertoires like walking, grooming, standing, and rearing, and profiling individual behavior signatures like subtle inter-group differences by age. SUBTLE highlights the importance of temporal representation in the behavioral embedding space for human-like behavioral categorization.