Analysis of Eco-Hydrological Characteristics of the Four Famous Carps' Spawning Grounds in the Middle Reach of Yangtze River

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchiv

Spatial-Temporal Data Mining for Ocean Science: Data, Methodologies, and Opportunities

With the increasing amount of spatial-temporal~(ST) ocean data, numerous spatial-temporal data mining (STDM) studies have been conducted to address various oceanic issues, e.g., climate forecasting and disaster warning. Compared with typical ST data (e.g., traffic data), ST ocean data is more complicated with some unique characteristics, e.g., diverse regionality and high sparsity. These characteristics make it difficult to design and train STDM models. Unfortunately, an overview of these studies is still missing, hindering computer scientists to identify the research issues in ocean while discouraging researchers in ocean science from applying advanced STDM techniques. To remedy this situation, we provide a comprehensive survey to summarize existing STDM studies in ocean. Concretely, we first summarize the widely-used ST ocean datasets and identify their unique characteristics. Then, typical ST ocean data quality enhancement techniques are discussed. Next, we classify existing STDM studies for ocean into four types of tasks, i.e., prediction, event detection, pattern mining, and anomaly detection, and elaborate the techniques for these tasks. Finally, promising research opportunities are highlighted. This survey will help scientists from the fields of both computer science and ocean science have a better understanding of the fundamental concepts, key techniques, and open challenges of STDM in ocean

A Study on the Response of Oncomelania Hupensis Diffusion to the Flow Regime of Dongting Lake

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchiv

Circulating Exosomal microRNAs as Biomarkers of Systemic Lupus Erythematosus

OBJECTIVES: Many studies indicate that microRNAs (miRNAs) could be potential biomarkers for various diseases. The purpose of this study was to investigate the clinical value of serum exosomal miRNAs in systemic lupus erythematosus (SLE). METHODS: Serum exosomes were isolated from 38 patients with SLE and 18 healthy controls (HCs). The expression of miR-21, miR-146a and miR-155 within exosomes was examined by reverse transcriptionquantitative polymerase chain reaction (RT-qPCR). Using receiver operating characteristic (ROC) curves, we evaluated the diagnostic value of exosomal miRNAs. RESULTS: Exosomal miR-21 and miR-155 were upregulated (po0.01), whereas miR-146a expression (po0.05) was downregulated in patients with SLE, compared to that in HCs. The expression of miR-21 (po0.01) and miR155 (po0.05) was higher in SLE patients with lupus nephritis (LN) than in those without LN (non-LN). The analysis of ROC curves revealed that the expression of miR-21 and miR-155 showed a potential diagnostic value for LN. Furthermore, miR-21 (R=0.44, po0.05) and miR-155 (R=0.33, po0.05) were positively correlated with proteinuria. The expression of miR-21 was negatively associated with anti-SSA/Ro antibodies (R= 0.38, po0.05), and that of miR-146a was negatively associated with anti-dsDNA antibodies (R= 0.39, po0.05). CONCLUSIONS: These findings suggested that exosomal miR-21 and miR-155 expression levels may serve as potential biomarkers for the diagnosis of SLE and LN

Enhanced Adaptive Graph Convolutional Network for Long-Term Fine-Grained SST Prediction

Sea surface temperature (SST) prediction is an important task in monitoring marine environment and forecasting ocean disasters, and has received tremendous popularity in recent years. However, existing methods for SST prediction usually focus on short-term prediction (e.g., predicting the SST in future seven days) or long-term coarse-grained prediction (e.g., predicting the monthly average SST in the next half year), and cannot work well for long-term fine-grained SST prediction (e.g., predicting the daily SST in future 60 days) due to their inability of learning long-term fine-grained spatial and temporal dependencies in SST. To address this issue, we proposed an enhanced adaptive graph convolutional network (EA-GCN) for long-term fine-grained SST prediction. First, EA-GCN introduces a static graph to capture the long-term correlations between the SST in different sea regions and a dynamic graph to learn the short-term correlations. Then, EA-GCN designs the multibranch spatio-temporal embedding to learn the patterns in SST at different time granularities. Finally, the learned patterns are fused to make long-term fine-grained SST predictions. According to the experiments on two real SST datasets, EA-GCN largely outperforms state-of-the-art SST prediction methods when predicting the daily SST in future 60 days

STA-GAN: A Spatio-Temporal Attention Generative Adversarial Network for Missing Value Imputation in Satellite Data

Satellite data is of high importance for ocean environment monitoring and protection. However, due to the missing values in satellite data, caused by various force majeure factors such as cloud cover, bad weather and sensor failure, the quality of satellite data is reduced greatly, which hinders the applications of satellite data in practice. Therefore, a variety of methods have been proposed to conduct missing data imputation for satellite data to improve its quality. However, these methods cannot well learn the short-term temporal dependence and dynamic spatial dependence in satellite data, resulting in bad imputation performance when the data missing rate is large. To address this issue, we propose the Spatio-Temporal Attention Generative Adversarial Network (STA-GAN) for missing value imputation in satellite data. First, we develop the Spatio-Temporal Attention (STA) mechanism based on Graph Attention Network (GAT) to learn features for capturing both short-term temporal dependence and dynamic spatial dependence in satellite data. Then, the learned features from STA are fused to enrich the spatio-temporal information for training the generator and discriminator of STA-GAN. Finally, we use the generated imputation data by the trained generator of STA-GAN to fill the missing values in satellite data. Experimental results on real datasets show that STA-GAN largely outperforms the baseline data imputation methods, especially for filling satellite data with large missing rates

Detection of False Data Injection Attacks in Smart Grid Based on Joint Dynamic and Static State Estimation

Power system state estimation is an essential component of the modern power system energy management system (EMS), and accurate state estimation is an indispensable basis for subsequent work. However, the attacker can inject biases into measurements to launch false data injection attacks (FDIAs) in smart grids, which ultimately cause state estimates to deviate from security values. This paper proposed the joint use of static state estimation and dynamic state estimation to detect the FDIA, i.e. the joint use of weighted least squares (WLS) and extended Kalman filter (EKF) with exponential weighting function (WEKF), which improves the robustness of state estimation. Since the WLS estimation considers only the measurements at the current moment, the recursive feature of the WEKF enables the estimation process to involve both historical state and current measurements. Therefore, consistency tests and residual tests were performed using the estimations of WLS and WEKF to effectively detect FDIA. In addition, a cluster partitioning approach with approximate equal redundancy of subsystems is proposed to locate the FDIA. The detection of FDIA triggers the partitioning of the network system, and then the chi-square test is used separately in each sub-network to determine the location of FDIA. Finally, the experimental results in the IEEE-14 bus system and the IEEE-30 bus system demonstrate that the approach can effectively detect and locate FDIAs