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The upgrading of algae biocrude (obtained by hydrothermal liquefaction) was studied under mild conditions. Here we adopted a guard catalyst to protect the core catalyst and obtained algae biofuel with low heteroatom content at 350 °C. The nitrogen content of biofuel is 0.016 wt %, and the calculated oxygen content is less than 0.51 wt %. Furthermore, the effective yield from biocrude to a low-heteroatom-containing biofuel was close to the theoretical yield. Through a detailed study of the upgrading process, the content variation trend of biocrude and biofuel compositions and their potential reaction pathways were revealed. The results indicated that hydrogenation reactions almost finished after upgrading at 350 °C for 15–30 min.

Biocrudes derived from hydrothermal liquefaction (HTL) and lipid extraction (EXT) of the high-lipid Scenedesmus were hydrotreated to investigate the influences of the feedstock on the profiles of the hydrotreating biofuels.

Relation classification is a significant task within the field of natural language processing. Its objective is to extract and identify relations between two entities in a given text. Within the scope of this paper, we construct an artificial dataset (CS13K) for relation classification in the realm of cybersecurity and propose two models for processing such tasks. For any sentence containing two target entities, we first locate the entities and fine-tune the pre-trained BERT model. Next, we utilize graph attention networks to iteratively update word nodes and relation nodes. A new relation classification model is constructed by concatenating the updated vectors of word nodes and relation nodes. Our proposed model achieved exceptional performance on the SemEval-2010 task 8 dataset, surpassing previous approaches with a remarkable F1 value of 92.3%. Additionally, we propose the integration of a ranking-based voting mechanism into the existing model. Our best results are an F1 value of 92.5% on the SemEval-2010 task 8 dataset and a value 94.6% on the CS13K dataset. These findings highlight the effectiveness of our proposed models in tackling relation classification tasks.