In recent years, additive manufacturing has gained significant attention in the production of high-temperature and high-pressure mini-channel heat exchangers. Among the various techniques, selective laser melting (SLM) is widely utilized in metal additive manufacturing. This study experimentally investigates the flow and heat transfer performance of rectangular and circular mini-channel heat exchangers produced by SLM. The experiments were conducted under pressure conditions ranging from 8 to 10 MPa and temperatures between 200 and 310°C. Heat transfer correlations for supercritical carbon dioxide (SCO2) in both rectangular and circular mini-channels were refined. Additionally, flow characteristics without heat transfer were analyzed, revealing that the Darcy friction factor in both types of mini-channels initially increases with the Reynolds number (Re), followed by a decrease, and stabilizes at higher Re values.

A critical component of today's commercial search engines is an advertisement platform. The current state-of-the-art of such platforms is primarily based on advanced keyword matching to determine the relevance of advertisements for users' queries. However, such keyword matching techniques suffer from missing user intent when the query domain is visual as opposed to textual. To handle such a domain, we propose a new advertisement platform which allows search engine advertisers to bid on images instead of just plain text. The main components of this platform include an advertisement editorial tool, ROI detection, image content understanding, and image matching modules. This platform is suitable for application scenarios where images are the main input or consumed content, for example, in Multimedia Messaging Service (MMS) or content based image retrieval. We demonstrated the effectiveness of our proposed advertisement platform solution when used in a mobile visual search scenario involving querying for real world billboards.

This paper introduces the architecture of our location sensitive indexing model which is used in a platform designed to deliver advertisements to users who primarily utilize images as queries instead of textual keywords. The indexing model facilitates an advertiser's ability to bid on images, such as billboards or logos, in order to obtain user feedback in judging image attractiveness. Additionally, the model enables automatic evaluation of advertisement popularity by mining users' query logs, which is critical for generating advertisement recommendations. The location sensitive architecture of this model enables effective and efficient functionality in large-scale scenarios. In the model's structure, our Location Sensitive Visual Indexing model (LSVI) incorporates location information that subdivides geographical regions for precise and localized image matching. By collecting feedback from mobile users, location-based mining can also help discover popular advertisements as well as their representative images. We have deployed our platform into a real-world advertising system in Beijing, China, which demonstrates effective results in comparative studies with both alternative and state-of-the-art approaches.