The Three Kingdoms period of ancient China (208-280 AD) refers to the period  between Eastern Han (25–220 AD) and Jin dynasties (266–420), during which China was divided into Shu (221-263 AD), Wei  (220-266 AD) and Wu (222-280 AD) kingdoms, and then united as Jin dynasty. This paper constructs the quarterly series of alliance structures between the Three Kingdoms. By collecting and analyzing a total of two hundred and eighty-nine quarterly observations, the paper shows that the three most frequent alliance structures are ρ0: 1) the finest partition or no-alliance structure with 192 partitions; 2) Three partitions with Shu-Jin alliance and Wu singletion with 57 partions; 3) Wei-Wu alliance and one singletion Shu with 12 partions. It also shows that the observed changes in alliance structures were the consequence of a total of fifteen major battles fought by the three kingdoms. Such results serve as a contribution to the studies of applied game theory, alliance study, and the economic and military histories in ancient China.

This study investigates the impact of artificial intelligence (AI) integration on preventing employee burnout through a human-centered, multimodal approach. Given the increasing prevalence of AI in workplace settings, this research seeks to understand how various dimensions of AI integration—such as the intensity of integration, employee training, personalization of AI tools, and the frequency of AI feedback—affect employee burnout. A quantitative approach was employed, involving a survey of 320 participants from high-stress sectors such as healthcare and IT. The findings reveal that the benefits of AI in reducing burnout are substantial yet highly dependent on the implementation strategy. Effective AI integration that includes comprehensive training, high personalization, and regular, constructive feedback correlates with lower levels of burnout. These results suggest that the mere introduction of AI technologies is insufficient for reducing burnout; instead, a holistic strategy that includes thorough employee training, tailored personalization, and continuous feedback is crucial for leveraging AI’s potential to alleviate workplace stress. This study provides valuable insights for organizational leaders and policymakers aiming to develop informed AI deployment strategies that prioritize employee well-being.

Environmental Education (EE) programs are of crucial importance. EE are aimed at global citizenship to generate new knowledge and new, more participatory and conscious ways of acting in the environment. This study, therefore, wants to verify the effectiveness of a training intervention that is based on education on climate change issues and on the active participation of subjects in the dimension of the small psychological group. At the intervention 309 students took part, equally distributed by gender (52.1% males), 64.4% enrolled in primary school, 35.6% enrolled in lower secondary school. A quantitative protocol was administered to evaluate the effectiveness of the intervention. The study shows an increase in pro-environmental behaviors and their stability even after 15–30 days. The intervention seems to be effective in triggering pro-environmental behaviors and maintaining them in the following weeks. The results of this study highlight the need to develop environmental education pro-grams in schools to increase levels of knowledge and awareness on the issue of climate change.