Yi Zeng

The problem is a traditional Chinese temperament is extremely important issue, the problem in different stages of development of the academic performance of different forms, the article is mainly from the development of this problem starting in the Song Dynasty, Zhu Xi and then examines the efforts of scholars on the benevolence and Hunan Love relationship to the discussion, which reveals the importance of temperament and Taoist internal shunt: the Ikawa, under the represented school of Zhu Xi, the advocates work only under the Han Yi Munetomo learning, and ease up on them; are those who as for enlightenment, as the representative of the Wufeng up camp, just work on the idea of the Shih-Jen, learning under the school's self-denial inherent within it. This Taoist internal differences largely based on the different nature to grasp the problem, the article seeks to explore how this difference affects the diversion of Taoism. I think, what is crucially important and widely neglected in the research of traditional Chinese philosophy is the problem concerning about the relationship between these two notion: "Xing" and "Qing" . It could be discerned as a pivotal clue in different stages in the development of traditional Chinese philosophy. I would like to point out how it has manifested in the movement of Neo-Confucianism in Song dynasty, which has caused different branches of this philosophical movement. And I think it would be clear to understand their different interpretations of this problem, if we would pay attention to the disputation between Chu Hsi and Nan-hsuan on the distinction between the two notions "Ren" and "Ai" . The opposite standpoints in their treatment of these two notions "Ren" and "Ai" has revealed the different comprehensions of the fundamental problem about "Xing" and "Qing" between these two schools of Neo-Confucianism. The school represented by Chu Hsi emphasized the necessity to cultivate and discipline oneself in practice so as to able to approach the inner Goodness, that is, to "Shi Ren" . But the opposite perspective which was taken by "Hu-hsiang" scholars, especially that of Nan-hsuan, claimed that it is only through the comprehension of the Goodness, that is, only one has the ability to grasp the Goodness immediately, that he would do appropriately in practical life and thus it would be natural for him to obtain the practial wisdom or scrutiny and self-discipline which was heavily stressed by Chu Hsi. I hope that through my interpretation of the debate aroused by different perspectives about the distinction between " Ren "and" Ai "in Neo-Confucianism and the consequences caused by it, one would get the impression of the significance of the problem of" Xing "and" Qing "in the development of traditional Chinese philosophy

Since the latter half of the twentieth century, science fiction narratives centered on humanoid robots have continuously explored the future of human–machine symbiosis through embodied character design, providing a conceptual testing ground for real-world robotic development. This study focuses on the metaphorical mechanisms underlying the “quasi-body” of robots in such narratives, revealing how they challenge the stability of the human concept. By analyzing how various humanoid robotic figures in science fiction narratives are modeled upon the human “body” and how the robots’ “quasi-body” reciprocally reshape the concept of the “human”, this article employs a dual perspective integrating phenomenological embodiment theory and conceptual metaphor theory. The argument unfolds in three progressive stages: deconstructing the metaphorical imitation of human in robotic embodiment within science fiction narrative, critiquing the simplified functional body that overlooks the fundamental role of the body in cognition, and tracing the reverse influence of humanoid metaphor modeling on the conceptualization of the human. This study seeks to expose the intrinsic tensions embedded in bodily metaphorization within human–robot modeling. As human–robot/machine symbiosis becomes an increasingly normalized condition of existence, only by disrupting entrenched cognitive frameworks of body stereotype can we cultivate novel relational paradigms imbued with greater ethical imagination in the technological reality.

The growing momentum to introduce artificial intelligence (AI) education into early schooling raises critical concerns about ethical readiness. Teaching technical skills in the absence of moral development risks producing a generation ill-equipped to navigate the profound societal consequences of AI. In response to this challenge, we introduce the moral grounding model (MGM), a values-first model of education that prioritizes ethics, cultural awareness, and relational thinking before technical instruction. To support this model, we examine a range of Eastern educational traditions, demonstrating how moral and philosophical training historically preceded specialization. We further connect these traditions to contemporary AI ethics principles such as fairness, accountability, and safety, to demonstrate how foundational moral education can inform the ethical development, design, and governance of AI systems. The paper concludes by advocating a values-first curriculum that cultivates future technologists who are not only capable but ethically conscious and socially responsible.

Background In this paper, we explore the question “Why can’t AI be a coauthor?” and reveal a rarely discussed reason. Methods and Results First, allowing AI to be a coauthor disregards the uniquely human experience of writing texts. This means that human authors are seen as mere producers of texts rather than rational beings engaged in a value-added and humanized learning process expressed through the paper. The relationship between the human author and the thesis is reduced to a mere result of generation rather than a result of individual human critical thinking. Second, allowing AI to be a coauthor leads to self-delusion about one’s own rationality and thus violates the responsibility to understand the world correctly. In this process of self-deception, it is not as if those who grant AI coauthor status do not realize that AI is not the same as humans; however, they self-deceivingly assume that AI has the same internal state as humans. This means that the relationship between the author and the work is no longer seen as a position to be respected, but as something probabilistic and gamified. Conclusions Finally, we discuss the potential consequences of these rationales, concluding that including AI as a coauthor implies a disregard for humanization.

Background: Disclosure of AI use is seen as a sign of the author’s honesty and commitment to the principle of transparency. However, existing discussions have paid little attention to a special case: authors who honestly disclose their use of AI feel ashamed because of their honesty. Methods and Results: The main issue discussed in this paper is why authors experience shame in the process of responsible disclosure of AI use. We redefine this emotion and its causes from the perspective of moral emotions. We argue that current disclosure policies only emphasize honesty but do not address how this honesty should be fairly treated. Conclusions: Current disclosure guidelines should ensure that authors feel more secure when disclosing AI use honestly in academic papers, thereby promoting an effective and responsible culture of disclosure. This requires more constructive narrative support. Expressing appreciation and respect for the honesty represented by disclosure is an appropriate way to address the issues discussed in this paper.

Artificial intelligence (AI) is increasingly integrated into healthcare systems, offering advances in diagnosis, treatment, and operational efficiency. However, the cultural assumptions embedded in AI training data often misalign with the sociocultural realities of diverse patient populations—a phenomenon we term the Cultural Calibration Gap (CCG). This conceptual paper defines the CCG as a structural and epistemic deficit, distinct from algorithmic bias or dataset shift, that undermines clinical validity, patient trust, and health equity. We analyze how culturally uncalibrated AI systems exacerbate disparities in diagnosis and care, compromise informed consent, and erode trust, particularly in communities with collective, spiritual, or traditional health frameworks. To address the CCG, we propose a three-pronged strategy: epistemic inclusion of diverse knowledge systems and data sources, participatory design grounded in community-based research, and regulatory reform mandating cultural responsiveness in model validation and oversight. We argue that tackling the CCG is not merely an ethical imperative but a scientific necessity, aligning healthcare AI with the evolving vision of human-centric, adaptive Healthcare 5.0. Culturally calibrated AI systems promise not only improved precision and trustworthiness but also more equitable and contextually meaningful care across global health landscapes.

Jecker et al critically analysed the predominant focus on existential risk (X-Risk) in artificial intelligence (AI) ethics, advocating for a balanced communication of AI’s risks and benefits and urging serious consideration of other urgent ethical issues alongside X-Risk.1 Building on this analysis, we argue for the necessity of acknowledging the unique attention-grabbing attributes of X-Risk and leveraging these traits to foster a comprehensive focus on AI ethics. First, we need to consider a discontinuous situation that is overlooked in the article by Jecker et al. This discontinuity refers to the phenomenon where X-Risk is perceived as dominating the discourse, yet contrary to expectations, it does not lead to a significant allocation of social resources for specific risk management and practical initiatives. In both the specific realm of ethical AI initiatives and the broader scope of AI risk management, the responses to X-Risk do not have the advantage of prioritising the allocation of resources over other related risks.2 This discrepancy suggests that, in terms of actual social resource allocation, X-Risks do not receive commensurate resources relative to the attention they attract. Unlike other types of risks, X-Risk is perceived as a distant threat, which does not correspond with actual initiatives in terms of media exposure. Despite the prominence of the longtermism view in various media and public discourse, the X-Risk of AI often serves merely as a cautionary note or opinion about the current situation. This suggests that concerns about an AI-driven catastrophe have not been effectively translated into practical initiatives. The gap between the attention drawn and …

To better determine the history of modern birds, we performed a genome-scale phylogenetic analysis of 48 species representing all orders of Neoaves using phylogenomic methods created to handle genome-scale data.We recovered a highly resolved tree that confirms previously controversial sister or close relationships.We identified the first divergence in Neoaves, two groups we named Passerea and Columbea, representing independent lineages of diverse and convergently evolved land and water bird species. Among Passerea, we infer the common ancestor of core landbirds to have been an apex predator and confirm independent gains of vocal learning. Among Columbea, we identify pigeons and flamingoes as belonging to sister clades. Even with whole genomes, some of the earliest branches in Neoaves proved challenging to resolve, which was best explained by massive protein-coding sequence convergence and high levels of incomplete lineage sorting that occurred during a rapid radiation after the Cretaceous-Paleogene mass extinction event about 66 million years ago.

In the past few years, many students have begun to lose interest in science and information and engineering technology courses because they find them too boring and hard to learn. To strengthen this field of education and stimulate students? motivation and interest in learning, this study introduces a theoretical pedagogical framework based on cooperative learning theory and tailored to the realities of the university education system in China. In the framework, a group in a class is treated as a system that goes through different stages and is subject to different input instructions and output outcomes. Based on this structure, teachers? interventions serve as feedback for controlling inputs to promote the correct operation of the system. This study aims to promote the development of a cooperative learning model for university students. A case study of the application of the framework shows that it has positive and active effects on student learning

Editor's AbstractIn one of the very first reactions to Li Minghui’s criticism of Mainland New Confucianism (MNC), Zeng Yi emphasizes the ties between MNC and Han-dynasty “Classical Learning” (jingxue), as opposed to the basis of Mou Zongsan-style New Confucianism in Song-dynasty Neo-Confucian “Way learning” (Daoxue). He further connects the MNC approach with an institutional, “concrete continuation” of the Confucian tradition, as opposed to the abstract, philosophical approach of Mou Zongsan.This short essay, another of the immediate reactions to Li Minghui's criticism of Mainland New Confucianism, focuses on the distinction between “old” (or traditional) and “new” Confucianism, and their differing relations to liberal democracy. Fang identifies a tension in Li Minghui's attitude toward the “old,” since Li seems to want to have a connection with the tradition but also not to be bound by it.