Make More Connections: Urban Traffic Flow Forecasting with Spatiotemporal Adaptive Gated Graph Convolution Network

Urban traffic flow forecasting is a critical issue in intelligent transportation systems. Due to the complexity and uncertainty of urban road conditions, how to capture the dynamic spatiotemporal correlation and make accurate predictions is very challenging. In most of existing works, urban road network is often modeled as a fixed graph based on local proximity. However, such modeling is not sufficient to describe the dynamics of the road network and capture the global contextual information. In this paper, we consider constructing the road network as a dynamic weighted graph through attention mechanism. Furthermore, we propose to seek both spatial neighbors and semantic neighbors to make more connections between road nodes. We propose a novel Spatiotemporal Adaptive Gated Graph Convolution Network ( STAG-GCN ) to predict traffic conditions for several time steps ahead. STAG-GCN mainly consists of two major components: (1) multivariate self-attention Temporal Convolution Network ( TCN ) is utilized to capture local and long-range temporal dependencies across recent, daily-periodic and weekly-periodic observations; (2) mix-hop AG-GCN extracts selective spatial and semantic dependencies within multi-layer stacking through adaptive graph gating mechanism and mix-hop propagation mechanism. The output of different components are weighted fused to generate the final prediction results. Extensive experiments on two real-world large scale urban traffic dataset have verified the effectiveness, and the multi-step forecasting performance of our proposed models outperforms the state-of-the-art baselines.

Fault Detection and Diagnosis in Industrial Processes with Variational Autoencoder: A Comprehensive Study

This work considers industrial process monitoring using a variational autoencoder (VAE). As a powerful deep generative model, the variational autoencoder and its variants have become popular for process monitoring. However, its monitoring ability, especially its fault diagnosis ability, has not been well investigated. In this paper, the process modeling and monitoring capabilities of several VAE variants are comprehensively studied. First, fault detection schemes are defined in three distinct ways, considering latent, residual, and the combined domains. Afterwards, to conduct the fault diagnosis, we first define the deep contribution plot, and then a deep reconstruction-based contribution diagram is proposed for deep domains under the fault propagation mechanism. In a case study, the performance of the process monitoring capability of four deep VAE models, namely, the static VAE model, the dynamic VAE model, and the recurrent VAE models (LSTM-VAE and GRU-VAE), has been comparatively evaluated on the industrial benchmark Tennessee Eastman process. Results show that recurrent VAEs with a deep reconstruction-based diagnosis mechanism are recommended for industrial process monitoring tasks.

Reliability Evaluation of Cyber–Physical Power Systems Considering Supply- and Demand-Side Uncertainties

To reach effective monitoring and control, a physical power grid couples with a communication network and evolves into cyber–physical power systems (CPPS), but this cyber–physical interdependence may exacerbate failure on the physical/cyber side and may turn into a cascading failure. Furthermore, distributed generators (DGs) and plug-in hybrid electric vehicles (PHEVs) introduced into CPPS add uncertainties to both the supply side and demand side of power energy. In this paper, we detail the model of CPPS and its coupling mechanism in operation and discuss the propagation mechanism of cascading failure within and across a physical power grid and a communication network. For uncertainties of power energy in the supply and demand sides, the generation and load of each day are divided into 24 time segments for modeling. In the case study, the well-being criteria and reliability indexes are employed to analyze the effect of DGs and cyber–physical interdependence on the reliability of CPPS when DGs suffer aging failure and cyber attacks, and the simulations indicate that introducing DGs can effectively enhance the period of healthy and marginal states. Furthermore, cyber attacks can sharply destroy the CPPS compared with aging failure.

Non-Linear Employment Effects of Tax Policy

We study the non-linear propagation mechanism of tax policy in a heterogeneous agent equilibrium business cycle model with search frictions in the labor market and an extensive margin of employment adjustment. The model exhibits endogenous job destruction and endogenous hiring standards in the form of occasionally-binding zero-surplus constraints. After parameterizing the model using U.S. data, we find that the dynamic response of employment to a temporary change in the labor income tax is highly non-linear, displaying sizable asymmetries and state-dependence. Notably, the response to a tax rate cut is at least twice as large in a recession as in an expansion.

A Modeling Study of the Productivity of Horizontal Wells in Hydrocarbon-Bearing Reservoirs: Effects of Fracturing Interference

Commercial production from hydrocarbon-bearing reservoirs with low permeability usually requires the use of horizontal well and hydraulic fracturing for the improvement of the fluid diffusivity in the matrix. The hydraulic fracturing process involves the injection of viscous fluid for fracture initiation and propagation, which alters the poroelastic behaviors in the formation and causes fracturing interference. Previous modeling studies usually focused on the effect of fracturing interference on the multicluster fracture geometry, while the related productivity of horizontal wells is not well studied. This study presents a modeling workflow that utilizes abundant field data including petrophysical, geomechanical, and hydraulic fracturing data. It is used for the quantification of fracturing interference and its correlation with horizontal well productivity. It involves finite element and finite difference methods in the numeralization of the fracture propagation mechanism and porous media flow problems. Planar multistage fractures and their resultant horizontal productivity are quantified through the modeling workflow. Results show that the smaller numbers of clusters per stage, closer stage spacings, and lower fracturing fluid injection rates facilitate even growth of fractures in clusters and stages and reduce fracturing interference. Fracturing modeling results are generally correlated with productivity modeling results, while scenarios with stronger fracturing interference and greater stimulation volume/area can still yield better productivity. This study establishes the quantitative correlation between fracturing interference and horizontal well productivity. It provides insights into the prediction of horizontal well productivity based on fracturing design parameters.

OPEN High Gain Low-Cost Antenna Based on the Utilization of Diffracted Fields from Semi-Ring Shape Dielectric Edges

A simple antenna with a 20-dBi gain is proposed. A thorough analysis of the propagation mechanism accompanied by a unique physical insight is provided. The realized structure has a low profile, low-cost, and compact features, a detailed link to the Fresnel-Huygens principle is provided.

Lattice Numerical Simulations of Hydraulic Fracture Propagation and Their Geometry Evolution in Transversely Isotropic Formations

Accurate prediction of the fracture geometry before the operation of a hydraulic fracture (HF) job is important for the treatment design. Simplified planar fracture models, which may be applicable to predict the fracture geometry in homogeneous and continuous formations, fail in case of fractured reservoirs and laminated formations such as shales. To gain a better understanding of the fracture propagation mechanism in laminated formations and their vertical geometry to be specific, a series of numerical models were run using XSite, a lattice-based simulator. The results were studied to understand the impact of the mechanical properties of caprock and injection parameters on HF propagation. The tensile and shear stimulated areas were used to determine the ability of HF to propagate vertically and horizontally. The results indicated that larger caprock Young’s modulus increases the stimulated area (SA) in both vertical and horizontal directions, whereas it reduces the fracture aperture. Also, larger vertical stress anisotropy and tensile strength of caprock and natural interfaces inhibit the horizontal fracture propagation with an inconsiderable effect in vertical propagation, which collectively reduces the total SA. It was also observed that an increased fluid injection rate suppresses vertical fracture propagation with an insignificant effect on horizontal propagation. The dimensionless parameters defined in this study were used to characterize the transition of HF propagation behavior between horizontal and vertical HFs.

Role of convection–circulation coupling in the propagation mechanism of the Madden–Julian Oscillation over the Maritime Continent in a climate model

This study investigates the role of convection–circulation coupling on the simulated eastward propagation of the Madden–Julian Oscillation (MJO) over the Maritime Continent (MC). Experiments are conducted with the European Centre Hamburg Model Version 5 (ECHAM5) coupled with the one-column ocean model—Snow-Ice-Thermocline (SIT) and two different cumulus schemes, Nordeng-Tiedtke (E5SIT-Nord) and Tiedtke (E5SIT-Tied). During the early phase of MJO composites, the E5SIT-Nord simulation reveals stronger intraseasonal anomalies in the apparent heat source (Q1) over the convective center, however, the E5SIT-Tied produces a stronger background Q1, suggesting that deep convection prevails over the MC but does not couple with the MJO circulation. Similarly, in the E5SIT-Tied simulation, in-column moisture is kept mostly by local deep convection over the MC, which is in contrast to the well-correlated relationship between moisture anomaly and MJO circulation in E5SIT-Nord. A case study based on an observational MJO reveals similar biases concerning of convection–circulation coupling emerges within a few days of simulations. The E5SIT-Tied simulation produces weaker heating at the convective center of the MJO than the E5SIT-Nord a few days after model initiation, resulting weaker subsidence to the east and less favorable for propagation. The present findings highlight the instantaneous responses of cumulus parameterization schemes to MJO-related environmental changes can further affect intraseasonal variability through altering convection–circulation coupling over the MC. Physical schemes of moist convection are essential to realistically represent this coupling and thereby improve the simulation of the eastward propagation of the MJO.

IKN-CF: An Approach to Identify Key Nodes in Inter-Domain Routing Systems Based on Cascading Failures

Inter-domain routing systems is an important complex network in the Internet. Research on the vulnerability of inter-domain routing network nodes is of great support to the stable operation of the Internet. For the problem of node vulnerability, we proposed a method for identifying key nodes in inter-domain routing systems based on cascading failures (IKN-CF). Firstly, we analyzed the topology of inter-domain routing network and proposed an optimal valid path discovery algorithm considering business relationships. Then, the reason and propagation mechanism of cascading failure in the inter-domain routing network were analyzed, and we proposed two cascading indicators, which can approximate the impact of node failure on the network. After that, we established a key node identification model based on improved entropy weight TOPSIS (EWT), and the key node sequence in the network can be obtained through EWT calculation. We compared the existing three methods in two real inter-domain routing networks. The results indicate that the ranking results of IKN-CF are high accuracy, strong stability, and wide applicability. The accuracy of the top 100 nodes of the ranking result can reach 83.6%, which is at least 12.8% higher than the average accuracy of the existing three methods.