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Traffic flow forecasting is an essential aspect of intelligent traffic management. It enables timely and proactive management of modern transport systems, increasing efficiency and resilience. However, accurately predicting short-term traffic flow is challenging due to its uncertain and interconnected nature. Traditional methods like loop detectors and high-resolution cameras have limited scalability. To address this, we propose a two-stage approach using low-resolution surveillance cameras. The first stage involves a vision-based data extraction module with calibration, vehicle detection, and tracking. Integration of Region of Interest, fine-tuning, and post-processing improves the robustness of low-resolution videos. In the second stage, a novel deep learning model extracts spatio-temporal features from historical traffic data for short-term flow prediction. The proposed model outperforms the STGCN model, achieving an 11.19% increase in MAE, a 12.37% improvement in RMSE and a 4.97% reduction in inference time. These advances highlight its potential for further research and applications in the field. [ABSTRACT FROM AUTHOR]

IntroductionSarcopenia is a common age-related disease, defined as a decrease in muscle strength and function owing to reduced skeletal muscle. One way to diagnose sarcopenia is through gait analysis and foot-pressure imaging.Motivation and research gapWe collected our own multimodal dataset from 100 subjects, consisting of both foot-pressure and skeleton data with real patients, which provides a unique resource for future studies aimed at more comprehensive analyses. While artificial intelligence has been employed for sarcopenia detection, previous studies have predominantly focused on skeleton-based datasets without exploring the combined potential of skeleton and foot pressure dataset. This study conducts separate experiments for foot-pressure and skeleton datasets, it demonstrates the potential of each data type in sarcopenia classification.MethodsThis study had two components. First, we collected skeleton and foot-pressure datasets and classified them into sarcopenia and non-sarcopenia groups based on grip strength, gait performance, and appendicular skeletal muscle mass. Second, we performed experiments on the foot-pressure dataset using the ResNet-18 and spatiotemporal graph convolutional network (ST-GCN) models on the skeleton dataset to classify normal and abnormal gaits due to sarcopenia. For an accurate diagnosis, real-time walking of 100 participants was recorded at 30 fps as RGB + D images. The skeleton dataset was constructed by extracting 3D skeleton information comprising 25 feature points from the image, whereas the foot-pressure dataset was constructed by exerting pressure on the foot-pressure plates.ResultsAs a baseline evaluation, the accuracies of sarcopenia classification performance from foot-pressure image using Resnet-18 and skeleton sequences using ST-GCN were identified as 77.16 and 78.63%, respectively.DiscussionThe experimental results demonstrated the potential applications of sarcopenia and non-sarcopenia classifications based on foot-pressure images and skeleton sequences.

The rapid expansion of large urban areas underscores the critical importance of road infrastructure. An accurate understanding of traffic flow on road networks is essential for enhancing civil services and reducing fuel consumption. However, traffic flow is influenced by a complex array of factors and perpetually changing conditions, making comprehensive prediction of road network behavior challenging. Recent research has leveraged deep learning techniques to identify and forecast traffic flow and road network conditions, enhancing prediction accuracy by extracting key features from diverse factors. In this study, we performed short-term traffic speed predictions for road networks using data from Mobileye sensors mounted on taxis in Daegu City, Republic of Korea. These sensors capture the road network flow environment and the driver's intentions. Utilizing these data, we integrated convolutional neural networks (CNNs) with spatio-temporal graph convolutional networks (STGCNs). Our experimental results demonstrated that the combined STGCN and CNN model outperformed the standalone STGCN and CNN models. The findings of this study contribute to the advancement of short-term traffic speed prediction models, thereby improving road network flow management. [ABSTRACT FROM AUTHOR]

The rapid expansion of large urban areas underscores the critical importance of road infrastructure. An accurate understanding of traffic flow on road networks is essential for enhancing civil services and reducing fuel consumption. However, traffic flow is influenced by a complex array of factors and perpetually changing conditions, making comprehensive prediction of road network behavior challenging. Recent research has leveraged deep learning techniques to identify and forecast traffic flow and road network conditions, enhancing prediction accuracy by extracting key features from diverse factors. In this study, we performed short-term traffic speed predictions for road networks using data from Mobileye sensors mounted on taxis in Daegu City, Republic of Korea. These sensors capture the road network flow environment and the driver’s intentions. Utilizing these data, we integrated convolutional neural networks (CNNs) with spatio-temporal graph convolutional networks (STGCNs). Our experimental results demonstrated that the combined STGCN and CNN model outperformed the standalone STGCN and CNN models. The findings of this study contribute to the advancement of short-term traffic speed prediction models, thereby improving road network flow management.

Rumor source detection has long been an important but difficult problem. Due to the complexity of the underlying propagation model, most existing methods only rely on the limit observation of a single batch of single snapshot during the propagation process in the spatial graph networks, which neglects temporal dependency and temporal features of the rumor propagation process. Taking multiple batches of multiple snapshots as input can reveal the temporal dependency. Inspired by the traditional spatial-temporal graph convolution network (STGCN), which is a model that can combine spatial and temporal features. In this paper, we propose a STGCN based model called Spatio-Temporal Approximate Personalized Propagation of Neural Predictions (STAPPNP), which firstly learns both the spatial and temporal features automatically from multiple batches of multiple snapshots to locate the rumor source. As there are no input algorithms that are suitable for multiple batches of multiple snapshots to capture the feature of nodes’ connectivity in STAPPNP, we develop an input algorithm to generate a 4-dimensional input matrix from the multiple batches of multiple snapshots to feed the proposed model. Nonetheless, for deep learning models, such input of multiple batches of multiple snapshots requires multiple convolutional layers to extract spatial features. Too many convolutional layers can lead to over-smoothing and long training time. To address these issues, we improve the Spatio-Temporal-Convolutional(ST-Conv) block, in which we adopt the approximate personalized propagation of neural predictions in the spatial convolutional layer of STAPPNP. Our experimental results show that the accuracy of the rumor source detection is improved by using STAPPNP, and the speed of the training process of STAPPNP outperforms state-of-the-art deep learning approaches under the popular epidemic susceptible-infected (SI) and susceptible-infected-recovery (SIR) models in social networks. [ABSTRACT FROM AUTHOR]

In order to expand the prediction range of spatio-temporal graph convolutional networks and apply them to the multivariate time series prediction problems in the scenario of unknown correlation, this paper proposed a graph learning based spatio-temporal graph convolutional networks（GLB-STGCN）. The graph learning layer learned the graph adjacency matrix from the time series with the help of cosine similarity, then the graph convolution networks captured the interaction between multi-variables, and finally the multi-kernel time convolution networks captured the periodic characteristics of the time series for precise prediction. To verify the effectiveness of GLB-STGCN, this paper used 4 public datasets from astronomy, electricity, transportation and economy and 1 industrial production dataset for the prediction experiments. The results showed that GLB-STGCN outperforms the comparison methods, especially on astronomical datasets, with prediction errors reduced by 6.02%, 8.01%, 6.72%, and 5.31%, respectively. The experimental results proved that GLB-STGCN has a wider application range and better prediction effect, especially for time series prediction problems with obvious natural cycles. [ABSTRACT FROM AUTHOR]

Introduction: Sarcopenia is a common age-related disease, defined as a decrease in muscle strength and function owing to reduced skeletal muscle. One way to diagnose sarcopenia is through gait analysis and foot-pressure imaging., Motivation and Research Gap: We collected our own multimodal dataset from 100 subjects, consisting of both foot-pressure and skeleton data with real patients, which provides a unique resource for future studies aimed at more comprehensive analyses. While artificial intelligence has been employed for sarcopenia detection, previous studies have predominantly focused on skeleton-based datasets without exploring the combined potential of skeleton and foot pressure dataset. This study conducts separate experiments for foot-pressure and skeleton datasets, it demonstrates the potential of each data type in sarcopenia classification., Methods: This study had two components. First, we collected skeleton and foot-pressure datasets and classified them into sarcopenia and non-sarcopenia groups based on grip strength, gait performance, and appendicular skeletal muscle mass. Second, we performed experiments on the foot-pressure dataset using the ResNet-18 and spatiotemporal graph convolutional network (ST-GCN) models on the skeleton dataset to classify normal and abnormal gaits due to sarcopenia. For an accurate diagnosis, real-time walking of 100 participants was recorded at 30 fps as RGB + D images. The skeleton dataset was constructed by extracting 3D skeleton information comprising 25 feature points from the image, whereas the foot-pressure dataset was constructed by exerting pressure on the foot-pressure plates., Results: As a baseline evaluation, the accuracies of sarcopenia classification performance from foot-pressure image using Resnet-18 and skeleton sequences using ST-GCN were identified as 77.16 and 78.63%, respectively., Discussion: The experimental results demonstrated the potential applications of sarcopenia and non-sarcopenia classifications based on foot-pressure images and skeleton sequences., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Naseem, Kim, Seo, Lee, Chung, Shin and Lee.)

The human cerebral cortex is folded into two fundamentally anatomical units: gyri and sulci. Previous studies have demonstrated the genetical, structural, and functional differences between gyri and sulci, providing a unique perspective for revealing the relationship among brain function, cognition, and behavior. While previous studies mainly focus on the functional differences between gyri and sulci under resting or task-evoked state, such characteristics under naturalistic stimulus (NS) which reflects real-world dynamic environments are largely unknown. To address this question, this study systematically investigates spatio-temporal functional connectivity (FC) characteristics between gyri and sulci under NS using a spatio-temporal graph convolutional network model. Based on the public Human Connectome Project dataset of 174 subjects with four different runs of both movie-watching NS and resting state 7T functional MRI data, we successfully identify unique FC features under NS, which are mainly involved in visual, auditory, emotional and cognitive control, and achieve high discriminative accuracy 93.06 % to resting state. Moreover, gyral regions as well as gyro-gyral connections consistently participate more as functional information exchange hubs than sulcal ones among these networks. This study provides novel insights into the functional brain mechanism under NS and lays a solid foundation for accurately mapping the brain anatomy-function relationship., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)