STGC-GNNs: A GNN-based traffic prediction framework with a spatial-temporal Granger causality graph

The key to traffic prediction is to accurately depict the temporal dynamics of traffic flow traveling in a road network, so it is important to model the spatial dependence of the road network. The essence of spatial dependence is to accurately describe how traffic information transmission is affected by other nodes in the road network, and the GNN-based traffic prediction model, as a benchmark for traffic prediction, has become the most common method for the ability to model spatial dependence by transmitting traffic information with the message passing mechanism. However, existing methods model a local and static spatial dependence, which cannot transmit the global-dynamic traffic information (GDTi) required for long-term prediction. The challenge is the difficulty of detecting the precise transmission of GDTi due to the uncertainty of individual transport, especially for long-term transmission. In this paper, we propose a new hypothesis\: GDTi behaves macroscopically as a transmitting causal relationship (TCR) underlying traffic flow, which remains stable under dynamic changing traffic flow. We further propose spatial-temporal Granger causality (STGC) to express TCR, which models global and dynamic spatial dependence. To model global transmission, we model the causal order and causal lag of TCRs global transmission by a spatial-temporal alignment algorithm. To capture dynamic spatial dependence, we approximate the stable TCR underlying dynamic traffic flow by a Granger causality test. The experimental results on three backbone models show that using STGC to model the spatial dependence has better results than the original model for 45 min and 1 h long-term prediction.Comment: 14 pages, 16 figures, 4 table

Deciphering Building Information Modeling Evolution: A Comprehensive Scientometric Analysis across Lifecycle Stages

Building Information Modeling (BIM) has catalyzed transformative shifts across various industries, which has sparked broader research interests in the BIM lifecycle. However, studies that specify the stated requirements for different technologies and methodologies from the perspective of the BIM lifecycle and analyze research hotspots and future research trends at each stage are scarce. Employing scientometric theories and methods, this study conducts an in-depth comparative analysis of BIM lifecycle stages. The analysis encompasses several aspects like annual research output and knowledge flows, in the aim of unveiling disparities in the technological requirements, defining research boundaries, and illuminating lifecycle research trends. Findings indicate an ongoing surge in research across all BIM lifecycle stages with technologies like digital twins and artificial intelligence becoming prevailing trends. The cooperative design of BIM components, virtual-real world coordination, interactions among buildings, individuals, and environments, as well as the in-depth integration of BIM with the multifaceted fields of urban management have emerged as focal points in the planning, construction, management, and maintenance of BIM, respectively. Future BIM lifecycle research will necessitate interdisciplinary collaboration, emphasizing technological integration, common data environment (CDE) information sharing, open-source BIM/historic building information modeling (HBIM) system, and impactful exploration in areas like urban construction and historical preservation

Self-Supervised Spatiotemporal Masking Strategy-Based Models for Traffic Flow Forecasting

Traffic flow forecasting is an important function of intelligent transportation systems. With the rise of deep learning, building traffic flow prediction models based on deep neural networks has become a current research hotspot. Most of the current traffic flow prediction methods are designed from the perspective of model architectures, using only the traffic features of future moments as supervision signals to guide the models to learn the spatiotemporal dependence in traffic flow. However, traffic flow data themselves contain rich spatiotemporal features, and it is feasible to obtain additional self-supervised signals from the data to assist the model to further explore the underlying spatiotemporal dependence. Therefore, we propose a self-supervised traffic flow prediction method based on a spatiotemporal masking strategy. A framework consisting of symmetric backbone models with asymmetric task heads were applied to learn both prediction and spatiotemporal context features. Specifically, a spatiotemporal context mask reconstruction task was designed to force the model to reconstruct the masked features via spatiotemporal context information, so as to assist the model to better understand the spatiotemporal contextual associations in the data. In order to avoid the model simply making inferences based on the local smoothness in the data without truly learning the spatiotemporal dependence, we performed a temporal shift operation on the features to be reconstructed. The experimental results showed that the model based on the spatiotemporal context masking strategy achieved an average prediction performance improvement of 1.56% and a maximum of 7.72% for longer prediction horizons of more than 30 min compared with the backbone models

Construction and validation of a fatty acid metabolism-related gene signature for predicting prognosis and therapeutic response in patients with prostate cancer

Background Reprogramming of fatty acid metabolism is a newly-identified hallmark of malignancy. However, no studies have systematically investigated the fatty acid metabolism related-gene set in prostate cancer (PCa). Methods A cohort of 381 patients with gene expression and clinical data from The Cancer Genome Atlas was used as the training set, while another cohort of 90 patients with PCa from GEO (GSE70769) was used as the validation set. Differentially expressed fatty acid metabolism-related genes were subjected to least absolute shrinkage and selection operator (LASSO)-Cox regression to establish a fatty acid metabolism-related risk score. Associations between the risk score and clinical characteristics, immune cell infiltration, tumor mutation burden (TMB), tumor immune dysfunction and exclusion (TIDE) score, and response to chemotherapy were analyzed. Finally, the expression level of genes included in the model was validated using real-time PCR. Results A prognostic risk model based on five fatty acid metabolism related genes (ALDH1A1, CPT1B, CA2, CROT, and NUDT19) were constructed. Tumors with higher risk score were associated with larger tumor size, lymph node involvement, higher Gleason score, and poorer biochemical recurrence (BCR)-free survival. Furthermore, the high- and low-risk tumors exhibited distinct immune cell infiltration features and immune-related pathway activation. High-risk tumors were associated with favorable response to immunotherapy as indicated by high TMB and low TIDE score, but poor response to bicalutamide and docetaxel chemotherapy. Conclusion This study established a fatty acid metabolism-related gene signature which was predictive of BCR and response to chemotherapy and immunotherapy, providing a novel therapeutic biomarker for PCa