SMAP: A Novel Heterogeneous Information Framework for Scenario-based Optimal Model Assignment

The increasing maturity of big data applications has led to a proliferation of models targeting the same objectives within the same scenarios and datasets. However, selecting the most suitable model that considers model's features while taking specific requirements and constraints into account still poses a significant challenge. Existing methods have focused on worker-task assignments based on crowdsourcing, they neglect the scenario-dataset-model assignment problem. To address this challenge, a new problem named the Scenario-based Optimal Model Assignment (SOMA) problem is introduced and a novel framework entitled Scenario and Model Associative percepts (SMAP) is developed. SMAP is a heterogeneous information framework that can integrate various types of information to intelligently select a suitable dataset and allocate the optimal model for a specific scenario. To comprehensively evaluate models, a new score function that utilizes multi-head attention mechanisms is proposed. Moreover, a novel memory mechanism named the mnemonic center is developed to store the matched heterogeneous information and prevent duplicate matching. Six popular traffic scenarios are selected as study cases and extensive experiments are conducted on a dataset to verify the effectiveness and efficiency of SMAP and the score function

Hydrogels enable negative pressure in water for efficient heat utilization and transfer

Metastable water in negative pressure can provide giant passive driving pressure up to several megapascals for efficient evaporation-driven flow, however, the practical applications with negative pressure are rare due to the challenges of generating and maintaining large negative pressure. In this work, we report a novel structure with thin hydrogel films as evaporation surfaces and robust porous substrates as the supports, and obtain a high negative pressure of -1.61 MPa through water evaporation. Molecular dynamics simulations elucidate the essential role of strong interaction between water molecules and polymer chains in generating the negative pressure. With such a large negative pressure, we demonstrate a streaming potential generator that spontaneously converts environmental energy into electricity and outputs a voltage of 1.06 V. Moreover, we propose a "negative pressure heat pipe" for the first time, which achieves a high heat transfer density of 11.2 kW cm-2 with a flow length of 1 m, showing the potential of negative pressure in efficient heat utilization and transfer.Comment: 43 pages, 18 figure