南通理工学院（Nantong Institute of Technology , NIT）简称通理工，学院坐落于中国首批14个对外开放沿海城市——南通市，是经国家教育部批准建立的一所全日制普通本科高校，创办于2000年6月。学校是江苏省首批登记为事业单位法人的民办大学。 根据2020年4月学校官网显示，学校拥有南通和海安两个校区，占地面积68余万平米，建筑面积35余万平米。现有各类在校学生1万多人,设有机械工程学院、汽车工程学院、建筑工程学院、商学院、电气与能源工程学院、计算机与信息工程学院、基础教学学院、创新创业学院、继续教育学院等9个二级学院，设置本科专业31个。其中,机械工程和工商管理被评为江苏省“十三五”一级重点建设学科。
Fe-17Mn-6Si-9Cr-5Ni alloy with varying WC concentrations was fabricated via laser wire deposition additive manufacturing. Their tribological performance under oil-lubricated conditions was systematically evaluated to elucidate the influence of WC addition amount on the wear resistance of Fe-Mn-Si-Cr-Ni alloy. Results demonstrate that in the Fe-Mn-Si-Cr-Ni alloy, when the WC addition amount does not exceed 1% (by mass fraction), the hardness of the alloy increases with the increase in WC addition amount, but its wear resistance actually decreases. At this point, the wear resistance of the alloy is mainly influenced by the intrinsic friction-reducing and wear-resistant properties brought about by the stress-induced martensitic phase transformation in the iron-based memory alloy. However, when the WC addition amount is increased to 2% and 4%, the hardness of the alloy continues to increase with the increase in WC addition amount, and its wear resistance improves accordingly, which is still only approximately the same as when no additives are added. At this point, martensite laths can no longer be observed in the sub-surface layer of the friction wear. It can be considered that at this point, the wear resistance of the alloy is mainly influenced by the dispersion strengthening effect of the WC particles. Under the conditions studied in this paper, the alloy with 0.25% WC added has the best wear resistance.
As a global geo-environmental issue, land subsidence poses a severe challenge to urban safety and sustainable development. Traditional methods for land subsidence analysis face challenges in terms of speed and accuracy in big data processing and monitoring; it thus requires new approaches. Artificial intelligence (AI) technology, as a representative of new quality productive forces, offers advantages in data preprocessing, model construction, and trend forecasting. This paper aims to further integrate AI into the study of land subsidence through a literature review. Initially, the paper provided a background introduction to AI and analyzed its current application in the field of land subsidence. The application of AI in land subsidence from the perspectives of monitoring and early warning systems, as well as macroscopic and microscopic mechanism studies was then prospected, highlighting the potential of AI in improving efficiency and accuracy. Finally, this review analyzed the limitations of AI and proposed suggestions in the future. It states that the study on land subsidence requires further assistance from AI technology. A deeper integration of AI technology into land subsidence analysis will not only improve data processing capabilities but also provide new insights into the early warning and effective control of land subsidence, contributing to the advancement and development of the field of land subsidence.
