Publications

OBJECTIVE To investigate the inhibitory effects of single and compound phenolic acids on mixed algae. METHODS Salicylic acid, cinnamic acid and pyrogallic acid were chosen individually or in pairs to act on mixed algae of Microcystis aeruginosa and Chlorella pyrenoidosa. RESULTS Three phenolic acids that singled or paired showed certain inhibitory effects on the mixed algae, of which were pyrogallic acid > salicylic acid > cinnamic acid in single phenolic acid, the EC50s of the three phenolic acids on the mixture of Microcystis aeruginosa and Chlorella pyrenoidosa were 7.87, 64.90 and 89.34 mg/L at the day 6; and inhibition of the paired phenolic acid group was pyrogallic acid & salicylic acid group > pyrogallic acid & cinnamic acid group > salicylic acid & cinnamic acid, the first 2 groups had synergistic algal inhibitory effects and the last paired group showed additive action. CONCLUSION Some specifically combined phenolic acids can lead to synergistic inhibiting effects on algae, which has better environmental safety for application due to the use of single phenolic acid was massively decreased.

Visual Question Answering (VQA) can facilitate social convenience, which needs to study complex joint reasoning in the visual and language over external knowledge. Recently, Knowledge-Based VQA has attracted the attention of researchers. There are many sources of external knowledge, including visual, textual, and commonsense knowledge, which can effectively improve the reasoning ability of the VQA model. However, introducing different knowledge sources increases the probability of retrieving irrelevant facts and generating noise and further impacts the model's performance. Existing approaches use contrast and prompt learning, visual matrices, density retrieval, etc., to address the noise but bring complex processes. Furthermore, the knowledge representation in these approaches is limited to specific knowledge forms, such as the triple of the knowledge graphs. To address the challenges, we propose a multi-modal joint-guided (MMJG) external knowledge introduction method. The method is to select more relevant external knowledge to the current question through the attention of multi-modal information. Unlike any existing method, our approach learns an adaptive selection module to select external knowledge that is more relevant to the question. Our approach is not specific to a particular knowledge form. The comparison and ablation experiments on the benchmark dataset show that our method achieves better results and demonstrates that our method is more effective.

Medical Visual Question Answering (VQA) has emerged as a promising solution to enhance clinic-decision making and patient interactions. Given a medical image and a corresponding question, medical VQA aims to predict an informative answer by reasoning the visual and textual information. However, datasets with limited samples circumscribe the generalization of medical VQA, reducing its accuracy when applied to unseen medical samples. Existing works tried to solve this problem with meta-learning or self-supervised learning but still failed to achieve satisfactory performance on medical VQA with insufficient samples. To address this problem, we propose multimodal hierarchical knowledge distillation for medical VQA (MHKD-MVQA). In the primary novelty of MHKD-MVQA, we distill knowledge from not only the output but also the intermediate layers, which leverages the knowledge from limited samples to a greater extent. Meanwhile, medical images and questions are embedded in a shared latent space, enabling our model to tackle multimodal samples. We evaluate our model on two medical VQA datasets, VQA-MED 2019 and VQA-RAD, where MHKD-MVQA achieves state-of-the-art performance and outperforms baselines by 3.6% and 1.6%, respectively. The extensive experiments also highlight the generalization of knowledge distillation by analyzing the class activation maps on medical images concerning specific questions.

Large language models (LLMs) have demonstrated remarkable performance on question-answering (QA) tasks because of their superior capabilities in natural language understanding and generation. However, LLM-based QA struggles with complex QA tasks due to poor reasoning capacity, outdated knowledge, and hallucinations. Several recent works synthesize LLMs and knowledge graphs (KGs) for QA to address the above challenges. In this survey, we propose a new structured taxonomy that categorizes the methodology of synthesizing LLMs and KGs for QA according to the categories of QA and the KG's role when integrating with LLMs. We systematically survey state-of-the-art methods in synthesizing LLMs and KGs for QA and compare and analyze these approaches in terms of strength, limitations, and KG requirements. We then align the approaches with QA and discuss how these approaches address the main challenges of different complex QA. Finally, we summarize the advancements, evaluation metrics, and benchmark datasets and highlight open challenges and opportunities.

No abstract is provided for this article.

No abstract is provided for this article.

Text-to-image (T2I) models are emerging as a powerful tool for designers to create user interface (UI) prototypes from natural language inputs (i.e., prompts). However, the discrepancy between designer inputs and model-preferred prompts makes it challenging for designers to consistently deliver effective results to end users. To bridge this gap, we introduce a novel hybrid method that assists designers in crafting user-centric prompts for T2I models, ensuring that the generated UIs align with end-user expectations. First, this method merges text mining and Kansei Engineering (KE) to analyze online user reviews and construct a Knowledge Graph (KG), mapping the intricate relationships between diverse affective requirements of users, design features, and corresponding text prompts for UI generation. Then, our approach automatically transforms designer inputs into model-preferred prompts through entity mention recognition and entity linking during the human-AI collaborative design process. Finally, we validate the proposed approach with a case study on automotive human–machine interface design. Experimental results demonstrate that our approach achieves high performance in perceived efficiency, satisfaction, and expectation disconfirmation. Overall, this study represents a step forward in integrating human and AI contributions in design and innovation within engineering disciplines, enabling AI to inspire, develop, and reinforce human creativity from a human factors perspective.