Jieling Liu

Based on the parallel classification of library borrowing data, the dynamic management and service ability of library borrowing information are improved, and a parallel classification method of library borrowing data based on Bayesian is proposed. According to the borrowing reports of user groups and individuals, a sampling model of library borrowing data is constructed, and a comprehensive borrowing index analysis model for users' personal construction system is established. Bayesian classification model is used to realize the feature mining and classification of borrowing data, and the correlation between different types of literature resources is mined according to the results of spectral feature extraction. Combined with the borrowing intensity between users with different attributes and different book classifications, Naive Bayesian classification algorithm is used to fully explore the internal relationship between fields. Based on the causal relationship between influencing factors, a dynamic model of parallel classification of library borrowing data is constructed, and the library borrowing data is matched according to certain rules. After weighted calculation, the reading behavior is quantified in the form of comprehensive index to realize parallel classification of library borrowing data. The test results show that the parallel classification of library borrowing data by this method improves the mining efficiency and accuracy of borrowing data, and the parallel processing efficiency is high.

Research on solutions for climate change in the urban transport sector has focused primarily on the technological and spatial planning perspectives; the social dimension of behavioral changes is relatively less explored, particularly in the southern European small city context. In this paper, we examine how social determinants and two other endogenous factors—motivation and knowledge—influence sustainable mobility choices in Portugal, through a case study of a city micro-mobility pilot, using the Elaboration Likelihood Model (ELM) and the Social Information Processing Theory (SIPT) as an analytical framework, and mix-method analysis from questionnaires, in-depth individual interviews, and other information collected throughout the pilot program. Motivation, as we found, was the strongest factor that contributed to comprehension, leading to a primarily positive attitude change towards pro-environmental behavior. Our research emphasizes the importance of mechanisms that stimulate citizens’ motivation and facilitate communication in sustainability pilot programs.