Hyperbolic Geometric Graph Representation Learning for Hierarchy-imbalance Node Classification

Learning unbiased node representations for imbalanced samples in the graph has become a more remarkable and important topic. For the graph, a significant challenge is that the topological properties of the nodes (e.g., locations, roles) are unbalanced (topology-imbalance), other than the number of training labeled nodes (quantity-imbalance). Existing studies on topology-imbalance focus on the location or the local neighborhood structure of nodes, ignoring the global underlying hierarchical properties of the graph, i.e., hierarchy. In the real-world scenario, the hierarchical structure of graph data reveals important topological properties of graphs and is relevant to a wide range of applications. We find that training labeled nodes with different hierarchical properties have a significant impact on the node classification tasks and confirm it in our experiments. It is well known that hyperbolic geometry has a unique advantage in representing the hierarchical structure of graphs. Therefore, we attempt to explore the hierarchy-imbalance issue for node classification of graph neural networks with a novelty perspective of hyperbolic geometry, including its characteristics and causes. Then, we propose a novel hyperbolic geometric hierarchy-imbalance learning framework, named HyperIMBA, to alleviate the hierarchy-imbalance issue caused by uneven hierarchy-levels and cross-hierarchy connectivity patterns of labeled nodes.Extensive experimental results demonstrate the superior effectiveness of HyperIMBA for hierarchy-imbalance node classification tasks.Comment: Accepted by Web Conference (WWW) 202

Environment-Aware Dynamic Graph Learning for Out-of-Distribution Generalization

Dynamic graph neural networks (DGNNs) are increasingly pervasive in exploiting spatio-temporal patterns on dynamic graphs. However, existing works fail to generalize under distribution shifts, which are common in real-world scenarios. As the generation of dynamic graphs is heavily influenced by latent environments, investigating their impacts on the out-of-distribution (OOD) generalization is critical. However, it remains unexplored with the following two major challenges: (1) How to properly model and infer the complex environments on dynamic graphs with distribution shifts? (2) How to discover invariant patterns given inferred spatio-temporal environments? To solve these challenges, we propose a novel Environment-Aware dynamic Graph LEarning (EAGLE) framework for OOD generalization by modeling complex coupled environments and exploiting spatio-temporal invariant patterns. Specifically, we first design the environment-aware EA-DGNN to model environments by multi-channel environments disentangling. Then, we propose an environment instantiation mechanism for environment diversification with inferred distributions. Finally, we discriminate spatio-temporal invariant patterns for out-of-distribution prediction by the invariant pattern recognition mechanism and perform fine-grained causal interventions node-wisely with a mixture of instantiated environment samples. Experiments on real-world and synthetic dynamic graph datasets demonstrate the superiority of our method against state-of-the-art baselines under distribution shifts. To the best of our knowledge, we are the first to study OOD generalization on dynamic graphs from the environment learning perspective.Comment: Accepted by the 37th Conference on Neural Information Processing Systems (NeurIPS 2023

Nonlinear Friction and Dynamical Identification for a Robot Manipulator with Improved Cuckoo Search Algorithm

This paper concerns the problem of dynamical identification for an industrial robot manipulator and presents an identification procedure based on an improved cuckoo search algorithm. Firstly, a dynamical model of a 6-DOF industrial serial robot has been derived. And a nonlinear friction model is added to describe the friction characteristic at motion reversal. Secondly, we use a cuckoo search algorithm to identify the unknown parameters. To enhance the performance of the original algorithm, both chaotic operator and emotion operator are employed to help the algorithm jump out of local optimum. Then, the proposed algorithm has been implemented on the first three joints of the ER-16 robot manipulator through an identification experiment. The results show that (1) the proposed algorithm has higher identification accuracy over the cuckoo search algorithm or particle swarm optimization algorithm and (2) compared to linear friction model the nonlinear model can describe the friction characteristic of joints better

Simulation of Water Environmental Capacity and Pollution Load Reduction Using QUAL2K for Water Environmental Management

In recent years, water quality degradation associated with rapid socio-economic development in the Taihu Lake Basin, China, has attracted increasing attention from both the public and the Chinese government. The primary sources of pollution in Taihu Lake are its inflow rivers and their tributaries. Effective water environmental management strategies need to be implemented in these rivers to improve the water quality of Taihu Lake, and to ensure sustainable development in the region. The aim of this study was to provide a basis for water environmental management decision-making. In this study, the QUAL2K model for river and stream water quality was applied to predict the water quality and environmental capacity of the Hongqi River, which is a polluted tributary in the Taihu Lake Basin. The model parameters were calibrated by trial and error until the simulated results agreed well with the observed data. The calibrated QUAL2K model was used to calculate the water environmental capacity of the Hongqi River, and the water environmental capacities of CODCr NH3-N, TN, and TP were 17.51 t, 1.52 t, 2.74 t and 0.37 t, respectively. The results showed that the NH3-N, TN, and TP pollution loads of the studied river need to be reduced by 50.96%, 44.11%, and 22.92%, respectively to satisfy the water quality objectives. Thus, additional water pollution control measures are needed to control and reduce the pollution loads in the Hongqi River watershed. The method applied in this study should provide a basis for water environmental management decision-making

Simulation of Water Environmental Capacity and Pollution Load Reduction Using QUAL2K for Water Environmental Management

In recent years, water quality degradation associated with rapid socio-economic development in the Taihu Lake Basin, China, has attracted increasing attention from both the public and the Chinese government. The primary sources of pollution in Taihu Lake are its inflow rivers and their tributaries. Effective water environmental management strategies need to be implemented in these rivers to improve the water quality of Taihu Lake, and to ensure sustainable development in the region. The aim of this study was to provide a basis for water environmental management decision-making. In this study, the QUAL2K model for river and stream water quality was applied to predict the water quality and environmental capacity of the Hongqi River, which is a polluted tributary in the Taihu Lake Basin. The model parameters were calibrated by trial and error until the simulated results agreed well with the observed data. The calibrated QUAL2K model was used to calculate the water environmental capacity of the Hongqi River, and the water environmental capacities of CODCr NH3-N, TN, and TP were 17.51 t, 1.52 t, 2.74 t and 0.37 t, respectively. The results showed that the NH3-N, TN, and TP pollution loads of the studied river need to be reduced by 50.96%, 44.11%, and 22.92%, respectively to satisfy the water quality objectives. Thus, additional water pollution control measures are needed to control and reduce the pollution loads in the Hongqi River watershed. The method applied in this study should provide a basis for water environmental management decision-making

Aqueous Fluid Connectivity in Subducting Oceanic Crust at the Mantle Transition Zone Conditions

Experiments were performed at 17-19 GPa and 1000-1200 degrees C to determine the aqueous fluid-majoritic garnet-majoritic garnet dihedral angle theta(grt-grt) in a basalt-H2O system. The results show that the theta(grt-grt) is between 44 +/- 2 degrees and 55 +/- 3 degrees, decreasing with increasing pressure and temperature. These new data combined with previous data obtained in the aqueous fluid-olivine and aqueous fluid-garnet systems suggest that connected network of aqueous fluids can form in the peridotite part of the subducting slab but may not form in a cold subducting oceanic crust at pressures below 14 GPa. Therefore, aqueous fluids formed by dehydration of a cold slab could be trapped as interstitial fluids in the oceanic crust and transported into the deep mantle. However, at the mantle transition zone (MTZ) conditions, aqueous fluids trapped previously and/or formed lately by the breakdown of hydrous minerals can percolate through the oceanic crust and hydrate the MTZ, providing an important mechanism for the MTZ hydration. Furthermore, aqueous fluids formed by mineral dehydration in a hot slab are readily lost into the mantle wedge at shallow depth, due to low dihedral angles (theta < 60 degrees) of the subducting oceanic crust, resulting in less water available to hydrate the MTZ. The distinct contribution of the cold slab and the hot slab to the MTZ hydration may cause water heterogeneity in the MTZ

Direct Shape Optimization and Parametric Analysis of a Vertical Inline Pump via Multi-Objective Particle Swarm Optimization

The vertical inline pump is a single-suction single-stage centrifugal pump with a curved inlet pipe before the impeller, which usually causes a significant increase of hydraulic losses in the inline pump. Considering the matching relationship between the inlet pipe and impeller, a multi-objective direct optimization based on the MOPSO of the inlet pipe and impeller was carried out to broaden the efficient operating area of the vertical inline pump. Bezier curves were adopted to control the profiles of the inlet pipe and impeller and 39 coordinates of the control points and the blade number were selected as the optimization variables. The efficiencies of the inline pump at the part-load and nominal conditions were chosen as the objective functions, which were obtained by the automatic simulation program. A dramatic improvement in pump performance was found after optimization. In the set of Pareto solutions, the maximum increases of efficiency at part-load and nominal conditions were 8.06% and 7.33% respectively. It also reported that the inlet pipe with longer horizontal length and lower bend curvature could reduce the hydraulic losses of the inlet pipe and increase the pump performance

Hydrochemical Characteristics and Genetic Mechanism of Geothermal Springs in the Aba Area, Western Sichuan Province, China

Geothermal resources have been a source of significant clean energy in the world. The Sichuan Province is famous for its abundant geothermal resources in China, especially in western Sichuan. The Aba area is a significant minority region in northwestern Sichuan with abundant geothermal resources. In this study, hydrochemical and D-O analyses were conducted on the eight collected geothermal springs to investigate the genetic mechanism of the geothermal resource in the Aba area. The exposed temperatures and pH values of the geothermal springs ranged from 23 °C to 48 °C and from 6.6 to 9.5, respectively. Based on the hydrochemical characteristics, the eight geothermal springs were classified into two types: class A and class B. The class A geothermal springs belonged to the hydrochemical type of Ca-Mg-HCO3-SO4 and Ca-Mg-HCO3 and were affected by the weathering and dissolution of carbonate and silicate. The class B hydrochemical type of geothermal spring was Na-HCO3, which was determined by the weathering and dissolution of evaporite and silicate. A Na-K-Mg triangle diagram revealed that the geothermal springs belonged to immature water. A chalcedony geothermometer indicated that the temperature of the class A shallow geothermal reservoir in the Aba area was 59.70–73.00 °C and 70.65–120.91 °C for class B. Silicon enthalpy approaches showed that the initial reservoir temperature for class A was 181.36–203.07 °C (mixed by 85.76–89.44% cold water) and 271.74–295.58 °C (mixed by 87.39–87.54% cold water) for class B. The recharge elevation of the geothermal spring was 3415–3495 m as calculated by the D-O isotopes. We have proposed these genetic models of the two typical geothermal springs. The achievements provide a vital reference for the further development of geothermal water and the sustainable utilization of geothermal resources in the Aba area

Parametric investigation and energy efficiency optimization of the curved inlet pipe with induced vane of an inline pump

The world energy consumption is currently growing at an alarming rate to support the increase of the world economy and population, which has brought a host of environmental issues. Improving energy efficiency is considered as the crucial solution for changing this situation. The widespread use of inline pumps in the water supply consumes a large amount of electricity, while the efficiency of such devices is lower than the average level. This research is aimed to study the relationship between the shape of the curved inlet pipe and the energy loss distributions by using flow loss visualization technology and correlation analysis. An induced vane was placed at the end of the inlet pipe to suppress the flow phenomena that cause efficiency losses. 700 designs of the inlet pipe with induced vane were generated and calculated to support the research using the automatic simulation approach. An optimization work was also presented to improve the comprehensive performance of the inline pump by using the multilayer feed-forward neural network and multi-objective particle swarm optimization. An excellent performance improvement was found after the optimization, and a deep analysis of four different design schemes based on the loss visualization method was presented to figure out the main reasons for hydraulic losses in the curved inlet pipe