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

Comparative Study between Two Schemes of Active-Control-Based Mechatronic Inerter

Based on force-current analogy and velocity-voltage analogy in the theory of electromechanical analogy, the inerter is a device that corresponded to the capacitor completely where conquers the nature restriction of mass, what’s more, it is significant to improve the ratio of the inerter’s inertance to its mass for mechanical networks synthesis. And according to the principle of active-control-based mechatronic inerter, we present two implementation schemes. One was based on linear motor, and the other was based on the ball screw and rotary motor. We introduced the implementation methods and established theoretical model of the two schemes, then compared the ratio of the inerter’s inertance to its mass for the two schemes. Finally, we consider the scheme is better which was based on the ball screw and rotary motor

Dual-acceptor alloy model delivers high detection performance of organic NIR detectors for real-time arterial pulse monitoring

Near-infrared organic photodetectors (NIR-OPDs) have significant potential in the fields of human sign monitoring, industrial defect detection, and military. We propose a method to construct high-performance NIR-OPDs by introducing narrow-band acceptor materials with very similar structures in bulk heterojunctions (BHJs) so that they form an alloy model during the film formation process, which in turn promotes the generation and dissociation of photogenerated excitons to achieve high-performance NIR detectors. Here, we choose the narrow-band materials IEICO-4F and IEICO-4Cl as dual-acceptors and PTB7-Th as the donor to construct NIR-OPDs. Benefiting from the alloy model formation, the dark current of the device is significantly suppressed compared with the binary control, while the photocurrent of the device is enhanced. The optimized NIR-OPD achieved a detectivity of more than 2.6×1012 Jones at -0.1V bias. With the optimized device performance, we can clearly monitor the human arterial pulse information, and the phases of the cardiac cycle of the heart can be accurately identified. This work demonstrates a new method for constructing highperformance NIR-OPDs and shows great potential for contactless human arterial pulse monitoring

A Class-Incremental Detection Method of Remote Sensing Images Based on Selective Distillation

With the rapid development of remote sensing technology and the growing demand for applications, the classical deep learning-based object detection model is bottlenecked in processing incremental data, especially in the increasing classes of detected objects. It requires models to sequentially learn new classes of objects based on the current model, while preserving old categories-related knowledge. Existing class-incremental detection methods achieve this goal mainly by constraining the optimization trajectory in the feature of output space. However, these works neglect the case where the previously learned background is a new category to learn, resulting in performance degradation in the new category because of the conflict between remaining the background-related knowledge or updating the background-related knowledge. This paper proposes a novel class-incremental detection method incorporated with the teacher-student architecture and the selective distillation (SDCID) strategy. Specifically, it is the asymmetry architecture, i.e., the teacher network temporarily stores historical knowledge of previously learned objects, and the student network integrates historical knowledge from the teacher network with the newly learned object-related knowledge, respectively. This asymmetry architecture reveals the significance of the distinct representation of history knowledge and new knowledge in incremental detection. Furthermore, SDCID selectively masks the shared subobject of new images to learn and previously learned background, while learning new categories of images and then transfers the classification results of the student model to the background class following the judgment model of the teacher model. This manner avoids interferences between the background category-related knowledge from a teacher model and the learning of other new classes of objects. In addition, we proposed a new incremental learning evaluation metric, C-SP, to comprehensively evaluate the incremental learning stability and plasticity performance. We verified the proposed method on two object detection datasets of remote sensing images, i.e., DIOR and DOTA. The experience results in accuracy and C-SP suggest that the proposed method surpasses existing class-incremental detection methods. We further analyzed the influence of the mask component in our method and the hyper-parameters sensitive to our method

A Kinematic Calibration Method of a 3T1R 4-Degree-of-Freedom Symmetrical Parallel Manipulator

This paper proposes a method for kinematic calibration of a 3T1R, 4-degree-of-freedom symmetrical parallel manipulator driven by two pairs of linear actuators. The kinematic model of the individual branched chain is established by using the local product of exponentials formula. Based on this model, the model of the end effector's pose error is established from a pair of symmetrical branched chains, and a recursive least square method is applied for the parameter identification. By installing built-in sensors at the passive joints, a calibration method for a serial manipulator is eventually extended to this parallel manipulator. Specifically, the sensor installed at the second revolute joint of each branched chain is saved, replaced by numerical calculation according to kinematic constraints. The simulation results validate the effectiveness of the proposed kinematic error modeling and identification methods. The procedure for pre-processing compensation on this 3T1R parallel manipulator is eventually given to improve its absolute positioning accuracy, using the inverse of the calibrated kinematic model

Research Progress on Effects of Ginsenoside Rg2 and Rh1 on Nervous System and Related Mechanisms

Neurological-related disorders are diseases that affect the body’s neurons or peripheral nerve tissue, such as Parkinson’s disease (PD) and Alzheimer’s disease (AD). The development of neurological disorders can cause serious harm to the quality of life and functioning of the patient. The use of traditional therapeutic agents such as dopamine-promoting drugs, anticholinergic drugs, cholinesterase inhibitors, and NMDA receptor antagonists is often accompanied by a series of side effects such as drug resistance, cardiac arrhythmia, liver function abnormalities, and blurred vision. Therefore, there is an urgent need to find a therapeutic drug with a high safety profile and few side effects. Herbal medicines are rich in active ingredients that are natural macromolecules. Ginsenoside is the main active ingredient of ginseng, which has a variety of pharmacological effects and is considered to have potential value in the treatment of human diseases. Modern pharmacological studies have shown that ginsenosides Rg2 and Rh1 have strong pharmacological activities in the nervous system, with protective effects on nerve cells, improved resistance to neuronal injury, modulation of neural activity, resistance to cerebral ischemia/reperfusion injury, improvement of brain damage after eclampsia hemorrhage, improvement of memory and cognitive deficits, treatment of AD and vascular dementia, alleviation of anxiety, pain, and inhibition of ionic-like behavior. In this article, we searched the pharmacological research literature of Rg2 and Rh1 in the field of neurological diseases, summarized the latest research progress of the two ginsenosides, and reviewed the pharmacological effects and mechanisms of Rg2 and Rh1, which provided a new way of thinking for the research of the active ingredients in ginseng anti-neurological diseases and the development of new drugs

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

Analysis of Urban Visual Memes Based on Dictionary Learning: An Example with Urban Image Data

The coexistence of different cultures is a distinctive feature of human society, and globalization makes the construction of cities gradually tend to be the same, so how to find the unique memes of urban culture in a multicultural environment is very important for the development of a city. Most of the previous analyses of urban style have been based on simple classification tasks to obtain the visual elements of cities, lacking in considering the most essential visual elements of cities as a whole. Therefore, based on the image data of ten representative cities around the world, we extract the visual memes via the dictionary learning method, quantify the symmetric similarities and differences between cities by using the memetic similarity, and interpret the reasons for the similarities and differences between cities by using the memetic similarity and sparse representation. The experimental results show that the visual memes have certain limitations among different cities, i.e., the elements composing the urban style are very similar, and the linear combinations of visual memes vary widely as the reason for the differences in the urban style among cities

Combined Microencapsulated Islet Transplantation and Revascularization of Aortorenal Bypass in a Diabetic Nephropathy Rat Model

Objective. Revascularization of aortorenal bypass is a preferred technique for renal artery stenosis (RAS) in diabetic nephropathy (DN) patients. Restenosis of graft vessels also should be considered in patients lacking good control of blood glucose. In this study, we explored a combined strategy to prevent the recurrence of RAS in the DN rat model. Methods. A model of DN was established by intraperitoneal injection of streptozotocin. Rats were divided into 4 groups: SR group, MIT group, Com group, and the untreated group. The levels of blood glucose and urine protein were measured, and changes in renal pathology were observed. The expression of monocyte chemoattractant protein-1 (MCP-1) in graft vessels was assessed by immunohistochemical staining. Histopathological staining was performed to assess the pathological changes of glomeruli and tubules. Results. The levels of urine protein and the expression of MCP-1 in graft vessels were decreased after islet transplantation. The injury of glomerular basement membrane and podocytes was significantly ameliorated. Conclusions. The combined strategy of revascularization and microencapsulated islet transplantation had multiple protective effects on diabetic nephropathy, including preventing atherosclerosis in the graft vessels and alleviating injury to the glomerular filtration barrier. This combined strategy may be helpful for DN patients with RAS