Comparative analysis of validation of compassion fatigue short scale and Professional Quality of Life Scale (ProQOL) in China under COVID-19 pandemic

Objective: To compare and analyze different existing compassion fatigue (CF) scales, and to test reliability and validity to find out a more suitable evaluation tool of CF for Chinese front-line medical staff. Method: With a sample of 252 Chinese clinicians (doctors and nurses), this study compared the validation of the two most used CF scales, Compassion Fatigue Short Scale (CF-Short Scale) and Professional Quality of Life Scale (ProQOL) in Chinese healthcare setting. Exploratory factor analysis, correlation analysis, and Cronbach's α were employed to examine the reliability and validity of the Chinese version of the CF scale by front-line clinical nurses and doctors. Lastly, One-way ANOVA test was conducted to examine and compare the CF scores of the medical staff with different characteristics. Finding: The Compassion Fatigue Short Scale included two factors, explaining totally 64.273％of the total variance, and Cronbach's α of C-CF Short Scale = 0.918, Job Burnout (JB) = 0.892, and Secondary Trauma (ST) = 0.909. The ProQOL (C-ProQOL) Scale also had a good internal consistency, with Cronbach's α of Compassion Satisfaction (CS) = 0.925, Secondary Trauma (ST) = 0.925, and Burnout (BO)= 0.705. However, the construct validity of C-ProQOL Scale was unsatisfactory with some problematic items. The CF scores among medical staff was at a medium level and differed significantly by the number of hours worked, the number of night shifts, and other characteristics of the medical staff. Conclusion: The C-CF Short Scale has better applicability that can be used as a reliable CF measurement for Chinese medical staff.Objectivo: Comparar e analisar as diferentes escalas de fadiga de compaixão (CF) existentes, e testar a fiabilidade e validade para descobrir uma ferramenta de avaliação mais adequada da CF para o pessoal médico chinês da linha de frente. Metodologia: Com uma amostra de 252 clínicos chineses (médicos e enfermeiros), este estudo comparou a validação das duas escalas CF mais utilizadas, a CF-Short Scale e a ProQOL Scale no contexto dos cuidados de saúde chineses. Análise exploratória dos factores, análise de correlação, e Cronbach's α were utilizado para examinar a fiabilidade e validade da versão chinesa da escala CF por enfermeiros clínicos e médicos da linha da frente. Por último, foi realizado o teste ANOVA unidireccional para examinar e comparar os resultados da CF do pessoal médico com características diferentes. Encontrar: A Escala Curta de Fadiga de Compaixão incluiu dois factores, explicando totalmente 64.273％of a variação total, e Cronbach's α da Escala Curta de C-CF = 0,918, Job Burnout (JB) = 0,892, e Secondary Trauma (ST) = 0,909. A Balança C-ProQOL também tinha uma boa consistência interna, com Cronbach's α de Compassion Satisfaction (CS) = 0,925, ST = 0,925, e Burnout(BO)= 0,705. No entanto, a validade de construção da Escala C-ProQOL foi insatisfatória com alguns itens problemáticos. A pontuação da CF entre o pessoal médico era de nível médio e diferia significativamente pelo número de horas trabalhadas, o número de turnos nocturnos, e outras características do pessoal médico. Conclusão: A C-CF Short Scale tem melhor aplicabilidade que pode ser usada como uma medição CF fiável para o pessoal médico chinês

Social Preferences in Behavioral Economics: The Study of Reciprocal Altruism under Different Conditions

Different external interventions prompt people to perceive different motivation which in turncauses different reactions. In our study, we propose that under different circumstances, the degree of the“reciprocal altruism heuristic” varies. This paper is aiming at carrying out an ultimatum game under twoscenarios and compares the results to demonstrate the effect of different external interventions on thetendency of reciprocal altruism. All 10 participants in the experiment, as a result, have shown differentinclination under the implementation of various external interventions, which strongly suggests the existenceof determinants that control the inclination of mutual cooperation and the provide insights for futurepsychological and educational related research to develop a more advanced system of human cognitivemodels under external interferences

On the Mechanics of NFT Valuation: AI Ethics and Social Media

As CryptoPunks pioneers the innovation of non-fungible tokens (NFTs) in AI and art, the valuation mechanics of NFTs has become a trending topic. Earlier research identifies the impact of ethics and society on the price prediction of CryptoPunks. Since the booming year of the NFT market in 2021, the discussion of CryptoPunks has propagated on social media. Still, existing literature hasn't considered the social sentiment factors after the historical turning point on NFT valuation. In this paper, we study how sentiments in social media, together with gender and skin tone, contribute to NFT valuations by an empirical analysis of social media, blockchain, and crypto exchange data. We evidence social sentiments as a significant contributor to the price prediction of CryptoPunks. Furthermore, we document structure changes in the valuation mechanics before and after 2021. Although people's attitudes towards Cryptopunks are primarily positive, our findings reflect imbalances in transaction activities and pricing based on gender and skin tone. Our result is consistent and robust, controlling for the rarity of an NFT based on the set of human-readable attributes, including gender and skin tone. Our research contributes to the interdisciplinary study at the intersection of AI, Ethics, and Society, focusing on the ecosystem of decentralized AI or blockchain. We provide our data and code for replicability as open access on GitHub.Comment: Presented at ChainScience Conference, 2003 (arXiv:2307.03277v2 [cs.DC] 11 Jul 2023

Multi-attribute Group Decision Making of Internet Public Opinion Emergency with Interval Intuitionistic Fuzzy Number

In this paper, an emergency group decision method is presented to cope with internet public opinion emergency with interval intuitionistic fuzzy linguistic values. First, we adjust the initial weight of each emergency expert by the deviation degree between each expert\u27s decision matrix and group average decision matrix with interval intuitionistic fuzzy numbers. Then we can compute the weighted collective decision matrix of all the emergencies based on the optimal weight of emergency expert. By utilizing the interval intuitionistic fuzzy weighted arithmetic average operator one can obtain the comprehensive alarm value of each internet public opinion emergency. According to the ranking of score value and accuracy value of each emergency, the most critical internet public emergency can be easily determined to facilitate government taking related emergency operations. Finally, a numerical example is given to illustrate the effectiveness of the proposed emergency group decision method

TGCnA: temporal gene coexpression network analysis using a low-rank plus sparse framework

Various gene network models with distinct physical nature have been widely used in biological studies. For temporal transcriptomic studies, the current dynamic models either ignore the temporal variation in the network structure or fail to scale up to a large number of genes due to severe computational bottlenecks and sample size limitation. Although the correlation-based gene networks are computationally affordable, they have limitations after being applied to gene expression time-course data. We proposed Temporal Gene Coexpression Network Analysis (TGCnA) framework for the transcriptomic time-course data. The mathematical nature of TGCnA is the joint modeling of multiple covariance matrices across time points using a ‘low-rank plus sparse’ framework, in which the network similarity across time points is explicitly modeled in the low-rank component. We demonstrated the advantage of TGCnA in covariance matrix estimation and gene module discovery using both simulation data and real transcriptomic data. The code is available at https://github.com/QiZhangStat/TGCnA

Backdooring Textual Inversion for Concept Censorship

Recent years have witnessed success in AIGC (AI Generated Content). People can make use of a pre-trained diffusion model to generate images of high quality or freely modify existing pictures with only prompts in nature language. More excitingly, the emerging personalization techniques make it feasible to create specific-desired images with only a few images as references. However, this induces severe threats if such advanced techniques are misused by malicious users, such as spreading fake news or defaming individual reputations. Thus, it is necessary to regulate personalization models (i.e., concept censorship) for their development and advancement. In this paper, we focus on the personalization technique dubbed Textual Inversion (TI), which is becoming prevailing for its lightweight nature and excellent performance. TI crafts the word embedding that contains detailed information about a specific object. Users can easily download the word embedding from public websites like Civitai and add it to their own stable diffusion model without fine-tuning for personalization. To achieve the concept censorship of a TI model, we propose leveraging the backdoor technique for good by injecting backdoors into the Textual Inversion embeddings. Briefly, we select some sensitive words as triggers during the training of TI, which will be censored for normal use. In the subsequent generation stage, if the triggers are combined with personalized embeddings as final prompts, the model will output a pre-defined target image rather than images including the desired malicious concept. To demonstrate the effectiveness of our approach, we conduct extensive experiments on Stable Diffusion, a prevailing open-sourced text-to-image model. Our code, data, and results are available at https://concept-censorship.github.io