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With the proposal of China's dual-carbon goal, the recovery of industrial waste heat (IWH) for district heating has gained increasing attention. Recently, some scholars have proposed that combined heat and water (CHW) systems are more economical compared with separate heat and water supplies. Beijing lacks desalinated water (DW) and clean low-carbon heat, whereas Tangshan in Hebei Province is rich in IWH. By combining CHW with seasonal thermal energy storage, a case of long-distance transport of hot DW from Tangshan to Beijing was planned, and the costs, energy consumption, and feasibility were analyzed. As a result, under the optimal heat source temperature and effect number of 70 °C and 10 for seawater desalination, the heat from various IWH sources of the three costal steel plants is recovered maximally to produce about 35000 t/h of 95 °C DW. Then, the hot DW is transported to Beijing with a long-distance heat loss ratio controlled below 3 %. Annually, 271.2 million tons of DW and 91.5 million GJ of heat is supplied. The introduction of IWH can save 2.68 billion Nm3 of natural gas per year and the discounted payback period is about 10 years, which has obvious energy-saving, environmental, and economic benefits. • Industrial waste heat is recovered for combined heat and water supply. • Annual utilization of waste heat from three iron and steel plants is maximized. • Thermal economic analysis of combined heat and water system is carried. • Economic long-distance transport radius reaches 200 km. • Seasonal thermal storage plays a dual role in heat storage and peak shaving. [ABSTRACT FROM AUTHOR]

In the past ten years, the application of artificial intelligence (AI) in biomedicine has increased rapidly, which roots in the rapid growth of biomedicine data, the improvement of computing performance, and the development of deep learning methods. At present, there are great difficulties in front of AI for solving complex and comprehensive medical problems. Ontology can play an important role in how to make machines have stronger intelligence and has wider applications in the medical field. By using ontologies, (meta) data can be standardized so that data quality is improved and more data analysis methods can be introduced, data integration can be supported by the semantics relationships which are specified in ontologies, and effective logic expression in nature language can be better understood by machine. This can be a pathway to stronger AI. Under this circumstance, the Chinese Conference on Biomedical Ontology and Terminology was held in Beijing in autumn 2019, with the theme "Making Machine Understand Data". The success of this conference further improves the development of ontology in the field of biomedical information in China, and will promote the integration of Chinese ontology research and application with the international standards and the findability, accessibility, interoperability, and reusability(FAIR) Data Principle. [ABSTRACT FROM AUTHOR]