Comparison of Genetic Algorithm Based Support Vector Machine and Genetic Algorithm Based RBF Neural Network in Quantitative Structure-Property Relationship Models on Aqueous Solubility of Polycyclic Aromatic Hydrocarbons

AbstractA modified method to develop quantitative structure-property relationship (QSPR) models of organic contaminants was proposed based on genetic algorithm (GA) and support vector machine (SVM). GA was used to perform the variable selection and SVM was used to construct QSPR model. In this study, GA-SVM was applied to develop the QSPR model for aqueous solubility (Sw, mg•l-1) of polycyclic aromatic hydrocarbons (PAHs). The R2 (0.980), SSE (2.84), and RMSE (0.25) values of the model developed by GA-SVM indicated a good predictive capability for logSw values of PAHs. Based on leave-one-out cross validation, the results of GA-SVM were compared with those of genetic algorithm-radial based function neural network (GA-RBFNN). The comparison showed that the R2 (0.923) and RMSE (0.485) values of GA-SVM were higher and lower, respectively, which illustrated GA-SVM was more suitable to develop QSPR model for the logSw values of PAHs than GA-RBFNN

心理资本干预对抑郁症患者的影响

Objective: To explore the effect of psychological capital intervention on the depressed patients. Method: 62 patients with depression were randomly divided into control group and experimental group. Control group was taken with drug treatment, experimental group was taken with drug treatment and psychological capital intervention. Two groups of patients had been evaluated by psychological capital questionnaire (PPQ) and depression self rating scale (SDS) , before and after treatment. Results: After treatment, the two groups of patients’ scores of PPQ and SDS both dropped significantly. The treatment results of the experimental group was better than the control group. Conclusion: In conventional drug treatment with psychological intervention of capital at the same time, can efetively improve the patients’ level of psychological capital, significantly alleviate symptoms of depression.目的：探讨心理资本干预在抑郁症治疗中的影响。方法：将62例患者随机分为对照组和实验组。对照组给予药物常规治疗，实验组在常规治疗的基础上同时辅以心理资本干预。两组均在首诊及治疗6周后，采用心理资本问卷（PPQ）和抑郁自评量表（SDS）进行评定。结果：治疗后，两组患者的心理资本问卷、抑郁自评表得分均下降显著；实验组效果明显优于对照组。结论：在常规药物治疗的同时辅以心理资本干预，能有效提高患者的心理资本水平，显著缓解抑郁症状

"I'm categorizing LLM as a productivity tool": Examining ethics of LLM use in HCI research practices

Large language models are increasingly applied in real-world scenarios, including research and education. These models, however, come with well-known ethical issues, which may manifest in unexpected ways in human-computer interaction research due to the extensive engagement with human subjects. This paper reports on research practices related to LLM use, drawing on 16 semi-structured interviews and a survey conducted with 50 HCI researchers. We discuss the ways in which LLMs are already being utilized throughout the entire HCI research pipeline, from ideation to system development and paper writing. While researchers described nuanced understandings of ethical issues, they were rarely or only partially able to identify and address those ethical concerns in their own projects. This lack of action and reliance on workarounds was explained through the perceived lack of control and distributed responsibility in the LLM supply chain, the conditional nature of engaging with ethics, and competing priorities. Finally, we reflect on the implications of our findings and present opportunities to shape emerging norms of engaging with large language models in HCI research