Effects of AI-Generated Content (AIGC) in the Game Development : From traditional PCG to AIGC

This paper aims to investigate the effect of AI-generated content (AIGC) when it starts to be applied in game development. AIGC in games refers to the generation of game content through artificial intelligence, a concept that has recently recieved a high level of attention due to the latest rapid developments in artificial intelligence, and in traditional research, AIGC can be categorized as an advanced approach to Procedural Content Generation (PCG), i.e., Deep Learning Method. Procedural Content Generation is the creation of game content through algorithms with limited or indirect user input. Its traditional approach has been widely used in games. Recently, however, the AIGC method has also started to be used by a large number of game companies, and its impact has exceeded expectations. A questionnaire survey of 40 game developers revealed a general interest in AIGC but also concerns. Further interviews explored the use of AIGC in game development and some of the problems it has encountered and predicted future trends in its development. The result of this study provide guidance on whether and how AIGC needs to be used in future game development

Effects of AI-Generated Content (AIGC) in the Game Development : From traditional PCG to AIGC

This paper aims to investigate the effect of AI-generated content (AIGC) when it starts to be applied in game development. AIGC in games refers to the generation of game content through artificial intelligence, a concept that has recently recieved a high level of attention due to the latest rapid developments in artificial intelligence, and in traditional research, AIGC can be categorized as an advanced approach to Procedural Content Generation (PCG), i.e., Deep Learning Method. Procedural Content Generation is the creation of game content through algorithms with limited or indirect user input. Its traditional approach has been widely used in games. Recently, however, the AIGC method has also started to be used by a large number of game companies, and its impact has exceeded expectations. A questionnaire survey of 40 game developers revealed a general interest in AIGC but also concerns. Further interviews explored the use of AIGC in game development and some of the problems it has encountered and predicted future trends in its development. The result of this study provide guidance on whether and how AIGC needs to be used in future game development

Association between urinary metal concentrations and abnormal estimated glomerular filtration rate in Chinese community-dwelling elderly: Exploring the mediating effect of triglycerides

Background: Environmental metal exposure is associated with elevated triglycerides (TG) and the development of chronic kidney disease (CKD). However, the relationship between metal exposure and glomerular filtration rate (GFR) remains uncertain, and the mediating effect of TG between the two is unclear. Methods: This study measured the concentrations of 14 metals in urine samples from 3752 elderly people in the community. The most relevant metals were screened by least absolute shrinkage and selection operator (LASSO) regression. The relationship between combined exposure to multiple metals and abnormal estimated glomerular filtration rate (eGFR) was explored using multivariate logistic regression analysis and Bayesian kernel machine regression (BKMR) analysis. Generalized linear regression models and the Karlson-Holm-Breen (KHB) method were used to assess the mediating effects of TG. Results: In the single-metal model, calcium (Ca), iron (Fe), selenium (Se), strontium (Sr), and thallium (Tl) showed significant negative correlations with the prevalence of abnormal eGFR (all P < 0.05). In the multi-metals model, Ca, Se, and Tl continued to show significant negative correlations, while vanadium (V) and zinc (Zn) showed significant positive correlations with abnormal eGFR (all P < 0.05). The BKMR model showed a negative joint effect of the mixture of Ca, V, Zn, Se, and Tl on the prevalence of abnormal eGFR. The generalized linear regression model showed a significant positive correlation between the concentrations of Ca (β = 0.07), Zn (β = 0.07), Se (β = 0.09), and TG levels (all P < 0.05). In the mediation analysis, TG masked a 4.30% and 5.21% correlation between Ca and Se and the prevalence of eGFR abnormalities, respectively. Conclusions: Urinary concentration of multiple metals is significantly associated with eGFR abnormalities, and Ca, and Se may be among the potential protective factors. TG masked some of the protective effects of Ca and Se