Traffic prediction plays an important role in Intelligent Transportation Systems (ITS). The main challenge lies in effectively capturing the dynamic multiple temporal periodic correlations and the long-range spatial correlation of traffic data. Despite the significant progress of many existing works, these methods often have two major limitations: 1) They mined the dynamic multi-period properties by using raw traffic sequences or the fixed periodicity strategy (e.g., hours, days, weeks), which failed to capture the dynamic multi-period characteristics of temporal correlation. 2) They mined the long-range spatial correlation of traffic data by stacking multilayer networks or directly using traditional similarity algorithms (e.g., conventional DTW). However, DTW has its own limitations leading to sub-optimal similarity assessment. To address these issues, we propose a periodic-aware spatial semantic transformer called PSformer for traffic prediction. Specifically, we propose the Periodic-aware Embedding Module (PAEmbed) to capture the dynamic multi-period properties by decoupling the traffic sequence into the multilevel frequency components via Fast Fourier Transform (FFT). In addition, we propose a Semantic Spatial Attention Mechanism (SSAM) to capture the long-range spatial correlation. In SSAM, we propose Time-weighted Dynamic Time Warping (TDTW) to model spatial correlations in semantically identical but geographically distant regions, which avoids considering two traffic patterns with large time spans as similar. Finally, to evaluate the performance of PSformer, we conduct extensive experiments on four real datasets. Experimental results show that our model achieves better performance than other state-of-the-art methods.

Traffic flow prediction provides valuable traffic information to transportation agencies and individuals in advance. Compared to next-step prediction, multi-step prediction provides users with traffic information for a longer time horizon, allowing users to have a more comprehensive understanding of traffic conditions. So far, various methods have been proposed for multi-step traffic flow prediction. However, most of them become sub-optimal in effectively detecting the spatio-temporal correlations of traffic data. Furthermore, as the number of prediction steps increases, the input data used to predict the flow of the next step tends to deviate further from the ground truth value. This deviation leads to a rapid decrease in prediction accuracy as the number of prediction steps increases. To address these issues, in this paper, we propose a deep spatio-temporal representation learning network named ST-RLNet for multi-step traffic flow prediction. The goal is to effectively generate the traffic data representation by better capturing the complex correlations of the data. In particular, we design a network called 3D-ConvLSTMNet to effectively extract short-term and long-term spatio-temporal data correlations for the next step prediction. To solve the performance degradation problem, we propose a feedback mechanism called PS-Feedback to dynamically reconstruct temporal correlation representations of input traffic flow for each round of next-step prediction. To evaluate the performance of the ST-RLNet, we conduct extensive experiments on two real-world datasets. Experimental results show that the ST-RLNet outperforms the state-of-the-art methods in both next-step and multi-step predictions, and exhibits consistent high performance under different traffic flows.

We perform a 2D Fourier transform along the horizontal direction on the 3D integration problem for the DC anomalous potential in the spatial domain, transforming it into a 1D integration problem with independent solutions at different wavenumbers. And the resulting 1D integral equation in the wavenumber domain is expressed as a sum of element integrals. We then employ the shape function method, using a quadratic shape function to characterize the scattering current density for each element and compute analytical expressions for the element integrals. Finally, we performed a 2D inverse Fourier transform of the wavenumber-domain anomalous potential and electric field to obtain their values in the spatial domain, which are modified using iterative operators. This approach fully leverages the efficiency of the Fourier transform method and the high accuracy of the shape function integration method to achieve faster and more accurate solutions to the 3D DC resistivity numerical simulation problem. Two examples demonstrate the accuracy and efficiency of our proposed algorithm. Numerical examples were used to analyze the relationship between the number of iterations and the anomaly conductivity difference during the convergence of the algorithm. It is shown that the algorithm converges faster for high resistance anomalies and can be applied to simulate models with significantly different resistivities between the background medium and the anomaly.

Personalized patient representation learning (PPRL) is a critical element in clinical risk prediction. It aims to obtain a complete portrait of each patient based on Electronic Health Records (EHR). Although existing works have achieved remarkable progress in healthcare prediction, there are still three major issues. First, feature correlation is crucial for risk prediction, but it has not yet been fully exploited by existing works. Second, variation pattern of dynamic feature contains useful information about patient's physical status, but adaptive pattern recognition is still a challenge. Third, existing works usually adopt a two-stage embedding process to process each dimension of the EHR data. However, some useful low-level information for PPRL will be lost. To address these issues, in this paper, we propose a fine-grained PPRL architecture named FG RL- N et for clinical risk prediction based on EHR. Specifically, we propose a Medical Feature Correlation Detection Module (FCM) to effectively learn the feature correlations for each patient and a Temporal Variation Pattern Recognition Module (TVM) to effectively detect the variation patterns of each dynamic feature. Moreover, we design a Fine-Grained Representation Mechanism (FGRM) to preserve the low-level information (from both feature and visit dimensions) useful for risk prediction. In addition, in the stage of data preprocessing, We utilize generic medical classification knowledge to classify numerical dynamic data. We conduct the in-hospital mortality experiment and the decompensation experiment on a real-world dataset. The experiment results show that the FGRL-Net outperforms state-of-the-art approaches. The source code is provided in github https://github.com/JackyChio/FGRL-Net.

Spatiotemporal correlations are crucial for traffic flow prediction. So far, various traffic flow prediction methods based on convolutional neural network (CNN) and long short-term memory (LSTM) network have been proposed. However, the common CNN - based models cannot preserve the temporal information after the first layer. Although the 3D CNN-based models can effectively capture short-term spatial and tempo-ral features, they are not suitable for long-term information capturing. LSTM is excellent at long-term features extraction. However, it alone cannot be used for spatial information extraction. To address these issues, we propose a deep architecture called 3D-ConvLSTMNet to better capture the spatiotemporal correlations among the traffic data. Specifically, we proposed a short-long term spatiotemporal feature extraction module called 3D-ConvLSTM, which uses 3D CNN to extract short-term spatiotemporal correlations, and uses ConvLSTM to extract the long-term spatiotemporal correlations. To get the long-distance spatial features, we adopt the residual neural network to develop the depth of 3D-ConvLSTMNet. Finally, we utilize a channel-wise attention mechanism to quantify the contribution of each grid in space domain. To evaluate the performances of ConvLSTMNet, we conduct extensive experiments on two real-world datasets. The experiment results show that our model gets better performances than the other state-of-the-art methods.

COVID-19 is a highly contagious virus. Blood test is one of effective methods for COVID-19 diagnosis. However, the issues of blood test are time-consuming and lack of medical staff. In this paper, four deep learning hybrid models are proposed to address these issues (i.e., CNN+GRU, CNN+Bi-RNN, CNN+Bi-LSTM, CNN+Bi-GRU). In addition, two best models, CNN and CNN+LSTM, from Turabieh et al. and Alakus et al., are implemented, respectively. Blood test data from Hospital Israelita Albert Einstein is used to train and test six models. The proposed best model, CNN+Bi-GRU, is accuracy of 0.9415, precision of 0.9417, recall of 0.9417, F1-score of 0.9417, AUC of 0.91, which outperforms the best models from Turabieh et al. and Alakus et al. Furthermore, the proposed model can help patients to get blood test results faster than traditional manual tests without errors caused by fatigue. The authors can envisage a wide deployment of proposed model in hospitals to alleviate the testing pressure from medical workers, especially in developing and underdeveloped countries.

Mutations in the Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) gene affect CFTR protein biogenesis or its function as a chloride channel, resulting in dysregulation of epithelial fluid transport in the lung, pancreas and other organs in cystic fibrosis (CF). Development of pharmaceutical strategies to treat CF requires understanding of the mechanisms underlying channel function. However, incomplete 3D structural information on the unique ABC ion channel, CFTR, hinders elucidation of its functional mechanism and correction of cystic fibrosis causing mutants. Several CFTR homology models have been developed using bacterial ABC transporters as templates but these have low sequence similarity to CFTR and are not ion channels. Here, we refine an earlier model in an outward (OWF) and develop an inward (IWF) facing model employing an integrated experimental-molecular dynamics simulation (200 ns) approach. Our IWF structure agrees well with a recently solved cryo-EM structure of a CFTR IWF state. We utilize cysteine cross-linking to verify positions and orientations of residues within trans-membrane helices (TMHs) of the OWF conformation and to reconstruct a physiologically relevant pore structure. Comparison of pore profiles of the two conformations reveal a radius sufficient to permit passage of hydrated Cl- ions in the OWF but not the IWF model. To identify structural determinants that distinguish the two conformations and possible rearrangements of TMHs within them responsible for channel gating, we perform cross-linking by bifunctional reagents of multiple predicted pairs of cysteines in TMH 6 and 12 and 6 and 9. To determine whether the effects of cross-linking on gating observed are the result of switching of the channel from open to close state, we also treat the same residue pairs with monofunctional reagents in separate experiments. Both types of reagents prevent ion currents indicating that pore blockage is primarily responsible.