With the popularity of sharing-based applications such as bicycle and car sharing, the sharing economy has attracted considerable global attention. The factors that affect users’ adoption of the sharing economy must be identified to facilitate the promotion of low-carbon lifestyles and help enterprises attract more active users. By employing the technology acceptance model (TAM) and herd behavior, this study implemented an expanded TAM and identified several factors affecting behavioral intention (BI) toward the sharing economy. A questionnaire was used to obtain the data, which were analyzed through structural equation modeling. The results revealed that perceived usefulness (PU) and perceived ease of use (PEOU) are the main factors affecting BI. Moreover, trust (TRU) was identified as a mediator of subjective norm (SN) and PEOU. Imitating others (IMI) affects BI, and SN affects TRU, PU, and PEOU. Gender moderates SN and IMI. This paper indicates that to improve users’ BI, enterprises should enhance PU, PEOU, and TRU; cooperate with organizations to enhance SN; and guide potential users to imitate others.

Identifying the key influencing factors that affected users’ adoption of sustainable mobile health care and improving alternative mobile health services continuously can help ordinary consumers (especially vulnerable populations) use extensive health resources cheaply and conveniently; which can increase the quality and equity of health services; so as to promote the sustainable development of medical resources. We integrated decision making trial and evaluation laboratory (DEMETEL)-based analytic network process (DANP) and a modified VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method to form a DDANPMV (DEMATEL+ DANP + Modified VIKOR) model to examine consumers’ adoption of mobile health care. This method was developed to not only help decision-makers evaluate alternative mobile health care and determine the best option, but also use to identify and improve the performance gap of alternative products with the goal of satisfying the aspirations of consumers. Four types of mobile health care products were analyzed. The results indicate that social norms; product image; and consumer trust are three key factors in consumer adoption. The results can guide policy design and implementation. The government and enterprises should improve mobile health care through these three aspects.

The reconstruction of traditional cities as smart cities has received considerable global attention because of the availability of new technology and the emerging Internet of Things (IoT). An evaluation index system is required to accurately measure the main factors that influence the sustainable development of smart cities in the real world. Therefore, this study proposes the analysis of influential relationships among criteria in the galactic model using the decision-making trial and evaluation laboratory (DEMATEL)-based analytic network process (DANP) method. An influential network relation map (INRM) is constructed to analyze relationships among indicators, after which the influential weights of the indicators are determined. An empirical case using the modified-vlsekriterijumska optimizacija i kompromisno resenje (VIKOR) method, comparing the development of Chinese cities Weihai and Qingdao, is used to validate the method. The results indicate that performance gaps exist among dimensions and among criteria, and improvement strategies are proposed that would reduce the performance gaps of these two smart cities. This is achieved by prioritizing the factors that required improvement. The proposed method thus provides a useful reference for continuous improvement in sustainable development planning toward the best smart city.

Pitting corrosion is one of the most prevalent and detrimental forms of localized corrosion in stainless steels exposed to chloride-containing environments. Its initiation and propagation are governed by the combined effects of alloy composition, environmental conditions, and experimental parameters. Although a substantial body of experimental data has been accumulated through decades of research, these data remain largely dispersed across the scientific literature, limiting their accessibility for large-scale statistical analyses and data-driven investigations. To address this challenge, this study proposes a large language model (LLM)-based framework for structured data extraction and database construction from scientific literature. By decomposing the complex information extraction process into multiple subtasks, including experimental condition extraction, pitting potential extraction, and data alignment, the framework enables the automated acquisition and integration of experimental data from unstructured scientific documents. Using the proposed framework, 12,792 candidate publications retrieved using a broad stainless-steel pitting corrosion search strategy were processed, resulting in a structured extraction output containing 36,095 experimental records. The database encompasses key information, including alloy compositions, environmental parameters, and pitting potentials. Stainless-steel-specific descriptive and statistical analyses were subsequently conducted using material-screened subsets of the extracted records. Data quality evaluation demonstrates that the proposed framework achieves strong aggregate performance in structured information extraction from complex scientific texts, while stage-specific validation indicates that pitting-potential extraction and normalization remain more challenging than the overall field-level extraction task. Statistical analysis of the resulting database reveals that existing studies are predominantly focused on room-temperature, neutral chloride environments, as well as austenitic and duplex stainless steel systems. Furthermore, partial correlation analysis revealed conditional associations between the investigated variables and pitting potential. Among these variables, molybdenum exhibited the strongest positive conditional association, whereas temperature showed a negative conditional association. Chloride ion concentration showed only a weak negative association. These trends were generally consistent with established corrosion knowledge and therefore provided a scientific plausibility check for the statistical patterns observed in the constructed database. This work highlights the potential of large language models for knowledge extraction and automated database construction from corrosion-related scientific literature. The proposed framework provides a reusable data resource and technical pathway for corrosion informatics research, while establishing a foundation for future data-driven studies on corrosion mechanism discovery, performance prediction, and materials design.