5,343 research outputs found
SAI: Solving AI Tasks with Systematic Artificial Intelligence in Communication Network
In the rapid development of artificial intelligence, solving complex AI tasks
is a crucial technology in intelligent mobile networks. Despite the good
performance of specialized AI models in intelligent mobile networks, they are
unable to handle complicated AI tasks. To address this challenge, we propose
Systematic Artificial Intelligence (SAI), which is a framework designed to
solve AI tasks by leveraging Large Language Models (LLMs) and JSON-format
intent-based input to connect self-designed model library and database.
Specifically, we first design a multi-input component, which simultaneously
integrates Large Language Models (LLMs) and JSON-format intent-based inputs to
fulfill the diverse intent requirements of different users. In addition, we
introduce a model library module based on model cards which employ model cards
to pairwise match between different modules for model composition. Model cards
contain the corresponding model's name and the required performance metrics.
Then when receiving user network requirements, we execute each subtask for
multiple selected model combinations and provide output based on the execution
results and LLM feedback. By leveraging the language capabilities of LLMs and
the abundant AI models in the model library, SAI can complete numerous complex
AI tasks in the communication network, achieving impressive results in network
optimization, resource allocation, and other challenging tasks
Evaluation of anti-smoking television advertising on tobacco control among urban community population in Chongqing, China
Background
China is the largest producer and consumer of tobacco in the world. Considering the constantly growing urban proportion, persuasive tobacco control measures are important in urban communities. Television, as one of the most pervasive mass media, can be used for this purpose.
Methods
The anti-smoking advertisement was carried out in five different time slots per day from 15 May to 15 June in 2011 across 12 channels of Chongqing TV. A cross-sectional study was conducted in the main municipal areas of Chongqing. A questionnaire was administered in late June to 1,342 native residents aged 18–45, who were selected via street intercept survey.
Results
Respondents who recognized the advertisement (32.77 %) were more likely to know or believe that smoking cigarettes caused impotence than those who did not recognize the advertisement (26.11 %). According to 25.5 % of smokers, the anti-smoking TV advertising made them consider quitting smoking. However, females (51.7 %) were less likely to be affected by the advertisement to stop and think about quitting smoking compared to males (65.6 %) (OR = 0.517, 95 % CI [0.281–0.950]). In addition, respondents aged 26–35 years (67.4 %) were more likely to try to persuade others to quit smoking than those aged 18–25 years (36.3 %) (OR = 0.457, 95 % CI [0.215–0.974]). Furthermore, non-smokers (87.4 %) were more likely to find the advertisement relevant than smokers (74.8 %) (OR = 2.34, 95 % CI [1.19–4.61]).
Conclusions
This study showed that this advertisement did not show significant differences on smoking-related knowledge and attitude between non-smokers who had seen the ad and those who had not. Thus, this form may not be the right tool to facilitate change in non-smokers. The ad should instead be focused on the smoking population. Gender, smoking status, and age influenced the effect of anti-smoking TV advertising on the general population in China
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