Forecasting traffic flow is significant for intelligent transportation systems (ITS), such as urban road planning, traffic control, traffic planning, and many more. A flow prediction model aims at forecasting the traffic flow of future time slices at certain regions by learning the historical traffic flow data and environmental information. However, due to the complicated traffic network topology and the dynamicity of traffic patterns in the real world, it is difficult to capture the multi-level spatial dependencies (e.g. global and local impacts to the traffic) and temporal dependencies (e.g. long-term and short-term impacts to the traffic). In this paper, we propose a Multi-step Coupled Graph Convolution Neural network (MCGCN) with temporal attention to capture the spatial and temporal dependencies of different levels in a traffic network, simultaneously, to predict traffic flow. First, a Multi-step Coupled Graph Convolution module (MCGC) is designed to learn the representation of a traffic network by coupling learning the relationship matrices, to capture the different levels’ information of a traffic network. Then, the traffic network information extracted by MCGC is fed into a Multi-step Coupled Graph Gated Recurrent Unit (MCGRU) module to realize the fusion of traffic network information and temporal features. Finally, a Multi-step Coupled Graph Attention mechanism (MCGCAtt) is used to extract the temporal information of historical time steps to predict the future traffic flow. The experiments are conducted on the NYCTaxi and NYCBike datasets, and the evaluation results demonstrate that our proposed model performs better than the nine compared methods.

FE models were developed to investigate the effect of processing and 3PB on the mechanical reliability of the embedded components. The results focus on the central region of resistor and capacitor. Local effects around the vias or copper layers are not considered. During processing, after processing and after bending compressive stresses are observed on both the embedded capacitor and the resistor. These results indicate that the risk of cracking on the components is low; however, the compressive stresses should be compared to the critical compressive stress limits of these materials. Similar results were obtained for the capacitor and the resistor. Due to the difference in the material properties (mainly CTE) higher compressive stresses are observed in the Alumina (resistor) compared to the Ceramic (capacitor). During processing, cooling causes the horizontal compressive stresses to increase, and heating causes them to decrease. Before the PCB is subjected to 3PB, the components possess compressive stresses which are caused by the lamination process. This is an advantage of the embedded components over a surface mounted component, where tensile stresses occur on the components. The 1mm bending loading causes the horizontal compressive stresses to reduce slightly. Bending the strip to 20 mm still does not cause the horizontal compressive stresses to diminish. The overall conclusion is that embedding the components is favorable to mounting a component on the surface of a PCB with respect to risk for component cracks. A high stiffness region was included in the model to represent the fiber glass around the components. This model was subjected to lamination process. The results show that the compressive stresses occurring on the components due to lamination is slightly affected by this region. Overall conclusions do not change with existence of a stiffer area above the components.

In recent years, traffic flow forecasting has attracted the great attention of many researchers with increasing traffic congestion in metropolises. As a hot topic in the field of intelligent city computing, traffic flow forecasting plays a vital role, since predicting the changes in traffic flow can timely alleviate traffic congestion and reduce the occurrence of accidents by vehicle scheduling. The most difficult challenges of traffic flow prediction are the temporal feature extraction and the spatial correlation extraction of nodes. At the same time, graph neural networks (GNNs) show an excellent ability in dealing with spatial dependence. Existing works typically make use of graph neural networks (GNNs) and temporal convolutional networks (TCNs) to model spatial and temporal dependencies respectively. However, how to extract as much valid information as possible from nodes is a challenge for GNNs. Therefore, we propose a multi-mode spatial-temporal convolution of mixed hop diffuse ODE (MHODE) for modeling traffic flow prediction. First, we design an adaptive spatial-temporal convolution module that combines Gate TCN and graph convolution to capture more short-term spatial-temporal dependencies and features. Secondly, we design a mixed hop diffuse ordinary differential equation(ODE) spatial-temporal convolution module to capture long-term spatial-temporal dependencies using the receptive field of the mixed hop diffuse ODE. Finally, we design a multi spatial-temporal fusion module to integrate the different spatial-temporal dependencies extracted from two different spatial-temporal convolutions. To capture more spatial-temporal features of traffic flow, we use the multi-mode spatial-temporal fusion module to get more abundant traffic features by considering short-term and long-term two different features. The experimental results on two public traffic datasets (METR-LA and PEMS-BAY) demonstrate that our proposed algorithm performs better than the existing methods in most of cases.