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.

Autonomous aerial vehicle (AAV) crowdsensing, as a complement to mobile crowdsensing, can provide ubiquitous sensing in extreme environments and has gathered significant attention in recent years. In this article, we investigate the issue of sensing data storage in AAV crowdsensing without edge assistance, where sensing data is stored locally in the AAVs. In this scenario, replication scheme is usually adopted to ensure data availability, and our objective is to find an optimal replica distribution scheme to maximize data availability while minimizing system energy consumption. Given the NP-hard nature of the optimization problem, traditional methods cannot achieve optimal solutions within limited timeframes. Therefore, we propose a centralized training and decentralized execution deep reinforcement learning (DRL) algorithm based on actor-critic, named “MUCRS-DRL.” Specifically, this method derives the optimal replica placement scheme based on AAV state information and data file information. Simulation results show that compared to the baseline methods, the proposed algorithm reduces data loss rate, time consumption, and energy consumption by up to 88%, 11%, and 11%, respectively.

Visual Crowdsensing (VCS) has gradually become an emerging research field as the built-in cameras of smart mobile devices have become a common recording tool in daily life. To meet the task requirements of VCS applications in the sensing process, sensing platforms usually use distributed data acquisition to collect image data from different sources. However, this leads to a large amount of redundant data in the final collected data set, which seriously affects the data quality. To solve the above problems, this paper proposes a dynamic redundancy detection method (VCSRD) for visual crowdsensing data based on contrastive learning. The method fuses the multimodal information of metadata and visual content through comparative clustering to realize the redundancy detection of data. Not only improves the accuracy of redundant data detection but also has some flexibility for data under different tasks. The effectiveness and flexibility of the proposed method under different data sizes are verified through experiments on real image datasets. Compared to the baseline method, VCSRD shows superior performance on all three clustering metrics.

Sparse Mobile Crowdsensing (Sparse MCS) is an emerging paradigm for data collection that significantly reduces the cost of smart city data acquisition while ensuring data quality by collecting data only from a subset of sensing cells and inferring data from the remaining unsensed cells using spatial and temporal correlations. Existing research has primarily focused on single sensing task scenarios, yet practical applications often involve multiple types of sensor data. These data types exhibit correlations that can be leveraged to optimize task allocation, thereby reducing sensing costs. This paper addresses the task assignment problem in multi-task scenarios, proposing the MDIA method based on information entropy theory and similarity matrices to finely assess the importance of data points. Building upon this, we further propose the MTA method, which employs a hybrid sensing mode (combining participatory sensing and opportunistic sensing) to select appropriate participants for sensing critical areas. Extensive experiments conducted on two real-world sensing datasets containing multiple data types validate the effectiveness of the proposed methods.

Abstract Patient representation learning based on electronic health records (EHR) is a critical task for disease prediction. This task aims to effectively extract useful information on dynamic features. Although various existing works have achieved remarkable progress, the model performance can be further improved by fully extracting the trends, variations, and the correlation between the trends and variations in dynamic features. In addition, sparse visit records limit the performance of deep learning models. To address these issues, we propose the multi-perspective patient representation Extractor (MPRE) for disease prediction. Specifically, we propose frequency transformation module (FTM) to extract the trend and variation information of dynamic features in the time–frequency domain, which can enhance the feature representation. In the 2D multi-extraction network (2D MEN), we form the 2D temporal tensor based on trend and variation. Then, the correlations between trend and variation are captured by the proposed dilated operation. Moreover, we propose the first-order difference attention mechanism (FODAM) to calculate the contributions of differences in adjacent variations to the disease diagnosis adaptively. To evaluate the performance of MPRE and baseline methods, we conduct extensive experiments on two real-world public datasets. The experiment results show that MPRE outperforms state-of-the-art baseline methods in terms of AUROC and AUPRC.

Patient representation learning based on electronic health records (EHR) is a critical task for disease prediction. This task aims to effectively extract useful information on dynamic features. Although various existing works have achieved remarkable progress, the model performance can be further improved by fully extracting the trends, variations, and the correlation between the trends and variations in dynamic features. In addition, sparse visit records limit the performance of deep learning models. To address these issues, we propose the Multi-perspective Patient Representation Extractor (MPRE) for disease prediction. Specifically, we propose Frequency Transformation Module (FTM) to extract the trend and variation information of dynamic features in the time-frequency domain, which can enhance the feature representation. In the 2D Multi-Extraction Network (2D MEN), we form the 2D temporal tensor based on trend and variation. Then, the correlations between trend and variation are captured by the proposed dilated operation. Moreover, we propose the First-Order Difference Attention Mechanism (FODAM) to calculate the contributions of differences in adjacent variations to the disease diagnosis adaptively. To evaluate the performance of MPRE and baseline methods, we conduct extensive experiments on two real-world public datasets. The experiment results show that MPRE outperforms state-of-the-art baseline methods in terms of AUROC and AUPRC.

Type 2 diabetes is the most common chronic disease for the elderly people. This disease is difficult to be cured and causes continued medical expenses. The early and personalized risk assessment of type 2 diabetes is necessary. So far, various type 2 diabetes risk prediction methods have been proposed. However, these methods have three major issues: 1) not fully considering the importance of personal information and rating information of healthcare system, 2) not adopting the long-term temporal information, and 3) not comprehensively capturing the correlation between the diabetes risk factor categories. To address these issues, the personalized risk assessment framework for elderly people with type 2 diabetes is needed. However, it is very challenging due to two reasons, namely imbalanced label distribution and high-dimensional features. In this paper, we propose diabetes mellitus network framework (DMNet) for type 2 diabetes risk assessment of elderly people. Specifically, we propose tandem long short-term memory to extract the long-term temporal information of different diabetes risk categories. In addition, the tandem mechanism is used to capture the correlation between the diabetes risk factor categories. To balance the label distribution, we adopt the method of synthetic minority over-sampling technique with Tomek links. To form the better feature representations, we utilize entity embedding to solve the problem of high-dimensional features. To evaluate the performance of our proposed method, we conduct the experiments on a real-world dataset called Research on Early Life and Aging Trends and Effects. The experiment results show that DMNet outperforms the baseline methods in terms of six evaluation metrics (i.e., accuracy of 0.94, balanced accuracy of 0.94, precision of 0.95, F1-score of 0.95, recall of 0.95 and AUC of 0.94).

This paper explores the impacts of the evolution of the dietary structure of the population on national food security using the food self-sufficiency rate under various statistical dimensions and simulate various food security scenarios for China in the medium- and long-term future. This study indicates that China’s rapid economic and social development and the continuous improvement in people’s living standards appears to be declining in the food self-sufficiency rate. The analysis regarding the factors influencing the food self-sufficiency rate revealed that the improved dietary structure, involving an increase in animal-based food product consumption, has led to a decrease in China’s food self-sufficiency rate. It concludes that the dietary structure is likely to continue to move toward increased consumption of animal-based food products in China for the future. The research results will assist decision-makers in formulating scientific food self-sufficiency rate targets and food security strategies

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.