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s an important force in maintaining social norms, third-party intervention has attracted widespread attention from researchers for its potential to promote prosocial behavior. To systematically examine the strength of this prosocial effect and its influencing factors, this study employed a three-level meta-analytic approach, synthesizing 130 effect sizes from 40 empirical studies involving a total of 10,289 participants. Main effect analyses revealed that third-party intervention has a moderately strong positive effect on prosocial behavior. Moderator analyses indicated that greater intensity and higher probability of third-party intervention were associate with stronger prosocial effects, whereas no significant moderating effects were observed for age, gender, intervention type, form, agent, cost, prosocial behavior measurement paradigm, or control group setting. These findings provide robust evidence for the positive impact of third-party intervention on prosocial behavior and clarify key moderating factors, offering valuable insights for future theoretical and experimental research.

Voluntary language switching refers to the language switching that occurs spontaneously among bilinguals. Compared with forced language switching, the costs of voluntary switching can be significantly reduced or even eliminated in contexts with greater freedom of choice or when switching is driven by bottom-up lexical access. Neuroimaging studies suggest that voluntary switching may engage the inhibitory control network to a lesser extent and additionally involves brain mechanisms related to self-generated intentions. The bilingual control mechanism in voluntary switching is closely related to second language proficiency, context, and individual executive control ability. Future research on voluntary language switching should integrate individual and contextual factors to develop more refined and dynamic models, combine real-world behavioral patterns with laboratory measures to deepen the investigation of the underlying neural mechanisms, conduct longitudinal studies and pay attention to the production-comprehension interactions. Such efforts may help to promote the transformation of voluntary language switching into cognitive benefits, fluid communication and effective learning.

his paper focuses on extracting "gene - disease - drug" triplet knowledge from biomedical literature based on knowledge graphs. The core tasks include mining "gene - disease" associations from PubMed abstracts, extracting "compound - disease" entities and relationships from PubMed literature, and identifying drug - drug interactions fromDrugBank database texts. By employing the Mixture of Experts (MoE) model and going through steps such as data preprocessing, model fine - tuning (e.g., LoRA for efficient parameter - efficient fine - tuning) and result evaluation, this project utilizes large language models to structurally extract key knowledge from biomedical literature, thereby supporting medicalresearch and knowledge discovery

his study presents a detailed case report of a 38-year-old male patient with coronary heart disease and heart failure. The patient exhibited symptoms including dyspnea, cough with phlegm, shortness of breath, and gout-related arthralgia. A combined approach integrating traditional Chinese medicine (TCM) and Western medicine was implemented in nursing care. Western medical interventions included diuretics, cardiac function improvement, sputum resolution, asthma relief, and uric acid control, while TCM provided comprehensive interventions through characteristic therapeutic techniques. The nursing process encompassed comprehensive assessment, accurate diagnosis, meticulous planning, active implementation, and effectiveness evaluation. The patient demonstrated significant clinical symptom improvement with favorable treatment outcomes, and remained stable during follow-up after discharge. This case highlights the crucial value of integrated TCM-Western medicine nursing in managing coronary heart disease-related heart failure, offering valuable reference for clinical practice.

s Artificial Intelligence (AI) technology rapidly advances and permeates various aspects of human life and work, it has emerged as a pivotal force driving productivity and societal transformation. However, amidst this widespread adoption, a crucial question arises: How can we foster high-quality and symbiotic interactive relationships between humans and AI? This inquiry transcends mere user experience and acceptance of AI technology; it also has profound implications for the technologys practical efficacy and future prospects. In response to this challenge, the concept of "Human-AI Rapport" (HAR) has emerged, serving as a bridge of professional interaction between human and AI.Integrating the concept of traditional interpersonal rapport and the uniqueness of human-AI interaction, this study defines HAR as the degree to which users experience (1) harmonious relationship, (2) mutual understanding and (3) tacit cooperation with AI in the process of achieving various professional work goals using AI. These constitute the three components of HAR. The concept of HAR not only extends the traditional framework of rapport in interpersonal relationships but also underscores the necessity for humanizing and intelligentizing AI developments.Due to doubts about the applicability of interpersonal rapport theories in human-AI interaction, previous research on HAR has lacked a systematic and fully developed theoretical model. This study combines the newly proposed concept and structure of HAR with the core ideas of Media Naturalness Theory (MNT) to propose a novel model for promoting HAR.The model has two layers: the outer layer details design objectives for human-AI interaction, including HAR (three evaluation dimensions) and media naturalness (connections between dimensions, indicating that achieving a particular objective can enhance corresponding perceptions of rapport). The inner layer outlines pathways to achieve these objectives through cognitive, social, and emotional intelligence of AI. From the inside out, through technological improvements, enhancements in these three types of intelligence will effectively achieve the three core interaction objectives in MNT, which ensure smooth human-AI communication, mimicking authentic interpersonal interaction, ultimately promoting HAR. Based on an integrative theoretical framework, AI should focus on harmonious relationship, mutual understanding and tacit cooperation in interacting with users within three core strategies of enhancing naturalness strategies:1.    Enhancing Cognitive Intelligence to Reduce Cognitive Effort: First, techniques should be employed on improving AIs ability to better understand users instructions and intentions, thereby enabling it to respond adaptively and appropriately. Second, AIs memory and recall capabilities should be enhanced to provide more contextual knowledge to facilitate communication. These reduce the cognitive effort required by users to explain, clarify, or repeat their requests, fostering a sense of being understood and perfect cooperation, which can better build a HAR.2.    Exhibiting Social Intelligence to Minimize Communication Ambiguity: AI can emulate nonverbal social cues such as facial expressions, gestures, and voice inflections, mirroring human behavior to increase social presence and reduce misunderstandings. Moreover, providing identity cues and self-disclosing information can further solidify the rapport between humans and AI.3.    Optimizing Emotional Intelligence to Elevate Physiological Arousal: AI that can recognize, interpret, and respond empathetically to human emotions triggers stronger emotional connections. By adjusting its responses based on users emotional states, AI fosters a more harmonious and empathic interaction experience.While existing research employs various questionnaires and behavioral indicators to measure HAR, there is a need for more authoritative and reliable measurement tools to ensure consistency and accuracy across studies. Furthermore, while increasing AIs intelligence is undoubtedly beneficial, the key lies in directing this intelligence towards enhancing the naturalness of human-AI interactions and HAR. Finally, In the future, AI will become more deeply integrated into human society and play more roles that require long-term establishment of HAR. Therefore, future research should focus on the HAR in more complex types of human-AI relationships.In conclusion, by proposing the concept and promotion model of HAR, this study provides a novel theoretical perspective for research on human-AI interaction and highlights urgent issues that need to be further explored and resolved in the future. It encourages the full realization of AIs potential and the expansion of its application scenarios and value boundaries, in order to facilitate the mutual adaptation between human society and AI technology, and to realize the vision of harmonious coexistence between humans and AI.