Indonesia ranks as the second-largest source of plastic garbage in marine areas, behind China. This is a critical problem that emphasises the need for synergistic endeavors to safeguard the long-term viability of marine ecosystems. The objective of this work is to examine the implementation of the Penta Helix model in the management of marine plastic trash. For this purpose, a Systematic Literature Review (SLR) was carried out, utilizing scholarly papers sourced from the Science Direct, Scopus, and Web of Science databases. The analysis centred on evaluating the Penta Helix model as a cooperative framework for tackling plastic waste management in the marine environments of Indonesia and China. The results suggest that the Penta Helix methodology successfully enables the amalgamation of many interests and resources, making a valuable contribution to the mitigation of plastic pollution in the waters of both nations. In order to advance a more comprehensive and sustainable approach to plastic waste management, this multidisciplinary plan brings together stakeholders from government, academia, business, civil society, and the media. Under this framework, the government is responsible for formulating laws, guidelines, and programs to decrease the use of disposable plastics and improve waste management infrastructure, all while guaranteeing adherence to environmental constraints. Simultaneously, the industrial and academic sectors are responsible for creating sustainable technology and pioneering business strategies, while civil society, in collaboration with the media, has a crucial role in increasing public consciousness regarding the destructive effects of plastic trash. This comprehensive strategy emphasizes the need of synergistic endeavors in tackling the intricate issues of marine plastic contamination.

Plastic products are items that we use every day around us, and their replacement speed are very fast, so that to recycle waste plastic has become the focus of environmental problems. This study has proposed an optimized circular design for the recycle plant of waste plastic, therefore, and our proposed strategy is to build a new tertiary recycling plant to reduce the total generation amount of the derived solid plastic waste from ordinary and secondary recycling plants and the semi-finished products from secondary recycling plant. Results obtained from a real recycle plant has showed that to recycle the tertiary waste plastic in a tertiary recycling plant, the finished products produced from a secondary recycling plant accounts about 27% of ordinary waste plastic, and the semi-finished products that mainly is scrap hardware accounts about 1% of ordinary waste plastic. Other derived solid plastic waste accounts for 6% of ordinary plastic waste. Therefore, if the ordinary, secondary and tertiary recycle plant can be set all-in-one, it can reduce the total generation amount of derived solid plastic waste from 34% to 6%, without and with a tertiary recycling plant, respectively. It can also increase the operating income of the secondary recycle plant and the investment willingness of the new tertiary recycle plant.

Cartography includes two major tasks: map making and map application, which is inextricably linked to artificial intelligence technology. The cartographic expert system experienced the intelligent expression of symbolism. After the spatial optimization decision of behaviorism intelligent expression, cartography faces the combination of deep learning under connectionism to improve the intelligent level of cartography. This paper discusses three problems about the proposition of “deep learning + cartography”. One is the consistency between the deep learning method and the map space problem solving strategy, based on gradient descent, local correlation, feature reduction and non-linear nature that answer the feasibility of the combination of “deep learning + cartography”; the second is to analyze the challenges faced by the combination of cartography from its unique disciplinary characteristics and technical environment, involving the non-standard organization of map data, professional requirements for sample establishment, the integration of geometric and geographical features, as well as the inherent spatial scale of the map; thirdly, the entry points and specific methods for integrating map making and map application into deep learning are discussed respectively.