A KP-mKP Hierarchy via Operators with Two Derivations

By using pseudo-differential operators containing two derivations, we extend the Kadomtsev-Petviashvili (KP) hierarchy to a certain KP-mKP hierarchy. For the KP-mKP hierarchy, we obtain its B\"{a}cklund transformations, bilinear equations of Baker-Akhiezer functions and Hirota equations of tau functions. Moreover, we show that this hierarchy is equivalent to a subhierarchy of the dispersive Whitham hierarchy associated to the Riemann sphere with its infinity point and one movable point marked

Mapping the water-economic cascading risks within a multilayer network of supply chains

Trade linkages within the supply chain can be mapped onto a complex network. Disruptions in regional resource supplies (i.e., water scarcity) have the potential to generate industrial losses in remote areas due to the interconnected flow of goods and services. While numerous studies have assessed the economic and virtual water supply networks, they assumed rapid and linear transmission of industrial risks in the network, without modeling the transmission process and vulnerability between nodes. Such oversights can lead to the misestimation of risks, especially in the context of climate change. Therefore, it is urgent to construct a cascading model for the supply economic and water supply network that consider step-by-step avalanche in nodes, to help identify vulnerable sectors and mitigate economic risks.In this research, we utilize the 2017 multi-region environmental multiregional input-output (E-MRIO) table in China to construct a comprehensive multilayer network. Each province is represented as a distinct layer within this network, incorporating 42 economic sectors(nodes). These layers and nodes are interconnected through trade linkages. To simulate the cascade process, we introduce the concept of net fragility for a node, calculated as the difference between the ratio of the sum of net inflows and net outflows of a node to its own total output and the threshold. Once a node fails (i.e., net fragility less than 1) the cascade process is triggered, then we quantify the total number of collapsed adjacent nodes, i.e., avalanche size. The bigger avalanche size refers to the province-sector higher vulnerability to economic shocks. Furthermore, we use the risk probability of province-sectors suffering from water scarcity as external shocks to describe the supply network response process under different water quantity and quality constraints. By comparing with the economic impact results, we can further identify vulnerable nodes affected by the dual restraints of water scarcity and economic shocks

Leakage control of water distribution system by drop-restore pressure based on viscoelastic mechanism

[EN] As a common method to control leakage of water distribution system, pressure management has the advantages of reducing energy consumption, reducing the possibility of explosion and avoiding the aggravation of leakage. In recent years, with the popularization of plastic pipe in the world, it is necessary to study its leakage characteristic. Our research group carried out leakage experiments on high density polyethylene (HDPE) pipe, and found that the correlation curve between leakage flow and pressure did not completely coincide in the phase of pressure boost and pressure reduction. The existing FAVAD and exponential leakage models could not explain this phenomenon, which challenges the pressure management theory dominated by a single depressing-pressure process, thus it’s necessary to explore pressure management strategies suitable for plastic pipes. Based on the viscoelastic properties of plastic pipe, we established the viscoelastic leakage model and proposed the leakage control method of drop-restore pressure, and verified its feasibility in practical engineering case. The main research objectives of this paper will be firstly to describe the strain response of leakage area in the process of continuous stress application with the Boltzmann superposition principle for HDPE pipe; the Voigt-Kelvin model is used to simulate the creep behavior of viscoelastic material, and a suitable leakage model for viscoelastic pipe is proposed to provide accurate expression of the leakage under the regulation of drop-restore pressure. Secondly, the viscoelastic pipe leakage model is embedded into the pressure-driven analysis model based on non-iterative method, and the pressure-driven viscoelastic leakage model is obtained. Finally, evaluating the proposed leakage model in the practical case. With the minimum of leakage flow as the objective function, the leakage control model of drop-restore pressure is established and solved by particle swarm optimization algorithm to obtain the accurate pressure regulation scheme. After applying the scheme from the optimization, the leakage rate decreases from 37.7% to 16.8% on weekday, which is great impact on leakage control.Gao, J.; Chen, J.; Wu, W.; Deng, L.; Li, K.; Yuan, Y. (2024). Leakage control of water distribution system by drop-restore pressure based on viscoelastic mechanism. Editorial Universitat Politècnica de València. https://doi.org/10.4995/WDSA-CCWI2022.2022.1481

ConvFormer: Revisiting Transformer for Sequential User Modeling

Sequential user modeling, a critical task in personalized recommender systems, focuses on predicting the next item a user would prefer, requiring a deep understanding of user behavior sequences. Despite the remarkable success of Transformer-based models across various domains, their full potential in comprehending user behavior remains untapped. In this paper, we re-examine Transformer-like architectures aiming to advance state-of-the-art performance. We start by revisiting the core building blocks of Transformer-based methods, analyzing the effectiveness of the item-to-item mechanism within the context of sequential user modeling. After conducting a thorough experimental analysis, we identify three essential criteria for devising efficient sequential user models, which we hope will serve as practical guidelines to inspire and shape future designs. Following this, we introduce ConvFormer, a simple but powerful modification to the Transformer architecture that meets these criteria, yielding state-of-the-art results. Additionally, we present an acceleration technique to minimize the complexity associated with processing extremely long sequences. Experiments on four public datasets showcase ConvFormer's superiority and confirm the validity of our proposed criteria

Time-interval visual saliency prediction in mammogram reading

Radiologists’ eye movements during medical image interpretation reflect their perceptual-cognitive behaviour and correlate with diagnostic decisions. Previous study has shown the significance of gaze behaviour of different time intervals for the decision-making process. Being able to automatically predict the visual attention of radiologists for different reading phases would enhance the reliability and explainability of artificial intelligence (AI) in diagnostic imaging. In this paper, we investigate the time-interval visual saliency in mammogram reading. We propose a novel visual saliency prediction model based on deep learning, which predicts a sequence of time-interval saliency maps for an input mammogram. Experimental results demonstrate the efficacy of the proposed time-interval saliency model