Online abnormal event detection of non-cooperative space targets based on space-based optical observations is an urgent requirement for ensuring space security, particularly for sudden state changes such as attitude and orbit maneuver events. However...
In recent years, graph neural networks (GNNS) have been demonstrated to be a powerful way to learn graph data. The existing recommender systems based on the implicit factor models mainly use the interactive information between users and items for tra...
In distribution systems, limited measurement configurations and communication constraints often result in a low success rate of data acquisition, posing challenges to both system observability and the real-time performance required by state estimatio...
The identification of drug–drug interactions (DDIs) plays a crucial role in various areas of drug development. In this study, a deep learning framework (KGCN_NFM) is presented to recognize DDIs using coupling knowledge graph convolutional netwo...
Recommender systems increasingly incorporate graph embeddings and graph neural networks to capture high-order relationships between users and items. However, the additional complexity of these approaches does not necessarily guarantee better recommen...
With the rapid development of online social networks recently, more and more online users have participated in social network activities and rich social relationships are formed accordingly. These social relationships provide a rich data source and r...
Dissolved oxygen (DO) is a crucial indicator for characterizing water quality and ecosystem status in freshwater lakes, and its concentration is closely correlated with the surrounding aquatic environment, particularly meteorological conditions. Howe...
The polar code has become one of the most popular and important forward error correction (FEC) coding schemes due to its symmetric characteristics of channel polarization. This paper reviews various decoding schemes for polar codes and discusses thei...
Accurate influenza forecasting is essential for public health emergency preparedness and timely resource allocation. Although meteorological factors are established modulators of influenza transmission, existing deep-learning models rarely exploit th...
Virulence factors (VFs), produced by pathogens, facilitate pathogenic microorganisms to invade, colonize, and damage the host cells. Accurate VF identification advances pathogenic mechanism understanding and provides novel anti-virulence targets. Exi...
Knowledge graph (KG) helps to improve the accuracy, diversity, and interpretability of a recommender systems. KG has been applied in recommendation systems, exploiting graph neural networks (GNNs), but most existing recommendation models based on GNN...
Graph neural networks (GNNs) have achieved great success in graph classification, with graph pooling methods being widely adopted for related tasks. Existing approaches typically rely on node ranking or clustering to coarsen graphs, but often fail to...
Sea surface temperature (SST) is a key factor in the marine environment, and its accurate forecasting is important for climatic research, ecological preservation, and economic progression. Existing methods mostly rely on convolutional networks, which...
In recent years, graph-based learning methods have gained significant traction in point-of-interest (POI) recommendation systems due to their strong generalization capabilities. These approaches commonly transform user check-in records into graph-str...
Recipe recommendation is the process of recommending suitable recipes to users based on factors such as user preferences and dietary needs. Recipes typically involve multiple modalities, with text and images being common, while most typical recipe re...
In the gas industry, accurate forecasting of gas production is critical for optimizing well operating conditions. Although traditional hydrodynamic models offer high accuracy, they are often computationally intensive and time-consuming, prompting a g...
Soybean yield prediction is one of the most critical activities for increasing agricultural productivity and ensuring food security. Traditional models often underestimate yields because of limitations associated with single data sources and simplist...
Anion exchange membranes (AEMs) play a vital role in the performance of water electrolyzers and fuel cells, yet their discovery and optimization remain challenging due to the complexity of structure–property relationships. In this study, we int...
While graph representation learning methods have been successfully applied to various graph data mining tasks, they typically couple graph information into an unstructured holistic representation. This makes it difficult to explicitly identify substr...
Background: Over the past few decades, micro ribonucleic acids (miRNAs) have been shown to play significant roles in various biological processes, including disease incidence. Therefore, much effort has been devoted to discovering the pivotal roles o...
Cable-stayed bridges are damaged by multiple factors such as natural disasters, weather, and vehicle load. In particular, if the stayed cable, which is an essential and weak component of the cable-stayed bridge, is damaged, it may adversely affect th...
In this paper, an assembly sequence planning system, based on artificial neural networks, is developed. The problem of artificial neural network itself is largely related to symmetry at every stage of its creation. A new modeling scheme, known as art...
Neural networks and deep learning have been successfully applied to tackle problems in drug discovery with increasing accuracy over time. There are still many challenges and opportunities to improve molecular property predictions with satisfactory ac...
The distribution network is the main component of the power system, which undertakes the important function of power transmission and distribution. Fast and accurate distribution network fault location is one of the important means to ensure the safe...
Air traffic management (ATM) relies on the running condition of the air traffic control sector (ATCS), and assessing whether it is overloaded is crucial for efficiency and safety for the entire aviation industry. Previous approaches to evaluating air...
Hydropower units are the core equipment of hydropower stations, and research on the fault prediction and health management of these units can help improve their safety, stability, and the level of reliable operation and can effectively reduce costs....
Link prediction is crucial in forecasting potential distribution channels within the dynamic and heterogeneous Xinjiang jujube sales market. This study utilizes knowledge graphs to represent entities and constructs a complex network model for market...
In urban road environments, global navigation satellite system (GNSS) signals may be interrupted due to occlusion by buildings and obstacles, resulting in reduced accuracy and discontinuity of combined GNSS/inertial navigation system (INS) positionin...
Loess deposits in China, covering extensive regions, exhibit distinctive physical and mechanical characteristics, including collapsibility and reduced mechanical strength. These properties contribute to heightened susceptibility to slope-related geol...
Autonomous indoor service robots are affected by multiple factors when they are directly involved in manipulation tasks in daily life, such as scenes, objects, and actions. It is of self-evident importance to properly parse these factors and interpre...
A back-propagation neural network prediction model with three layers and six neurons in the hidden layer is established to overcome the limitation of the equivalent linear overbreak slough (ELOS) empirical graph method in estimating unplanned ore dil...
Graph convolutional network (GCN) architecture is the basis of many neural networks and has been widely used in processing graph-structured data. When dealing with large and sparse data, deeper GCN models are often required. However, the models suffe...
Semantic segmentation of remote sensing images is challenging because multi-scale irregular objects in complex scenes often exhibit large intra-class variability, high inter-class similarity, and sparse spatial distributions. These factors hinder acc...
As the COVID-19 pandemic began spreading worldwide in late 2019 and early 2020, many vaccine candidates were developed to combat the disease. However, new COVID-19 variants such as Omicron and Delta continue to emerge globally despite advancements in...
In response to the insufficient consideration of spatio-temporal dependencies and traffic pattern similarity in traffic flow prediction methods based on federated learning, as well as the neglect of model heterogeneity and objective heterogeneity, a...
To fully leverage the spatiotemporal dynamic correlations in air traffic flow and enhance the accuracy of traffic flow prediction models, thereby providing a more precise basis for perceiving congestion situations in the air route network, a study wa...
Understanding the relationship between the chemical structure and physicochemical properties of odor molecules and olfactory perception, i.e., the structure–odor relationship, remains a decades-old, challenging task. However, the differences am...
The goal of aspect-based sentiment analysis (ABSA) is to identify the sentiment polarity of specific aspects in a context. Recently, graph neural networks have employed dependent tree syntactic information to assess the link between aspects and conte...
Centrality, community and reach analyses of social platforms are graph traversals whose latency depends on the underlying data infrastructure. This paper compares a native graph engine (Neo4j 5.26 with the Graph Data Science library) and a relational...
Traffic forecasting is a crucial component that underpins an intelligent transportation system. Among the current mainstream forecasting algorithms, spatial–temporal graph neural networks (STGNNs), as the mainstream solution, have been used in...
Floods are one of the most lethal natural disasters. It is crucial to forecast the timing and evolution of these events and create an advanced warning system to allow for the proper implementation of preventive measures. This work introduced a new gr...
Electricity load forecasting is of great significance for the overall operation of the power system and the orderly use of electricity at a later stage. However, traditional load forecasting does not consider the change in load quantity at each time...
In complex urban road environments, vehicles inevitably experience frequent or sustained interruptions of the Global Navigation Satellite System (GNSS) signal when passing through overpasses, near tall buildings, and through tunnels. This results in...
Accurately predicting industrial electricity consumption is of paramount importance for optimizing energy management and operational efficiency. Traditional forecasting approaches face significant challenges in capturing the complex factors influenci...
Photovoltaic power generation prediction is crucial for the effective integration of renewable energy into the grid, real-time grid balancing, and the optimization of energy storage systems. However, PV power generation is highly dependent on environ...
In today’s volatile business environment, securing a sustainable competitive advantage hinges on retaining and effectively managing talent. While talent turnover is inevitable, strategic internal human resource (HR) transfers offer a solution t...
This work addresses the problem of demand forecasting in supply chain management, where the consolidation of scattered and heterogeneous data and the lack of precise forecasting methods generate operational inefficiencies, resulting in increased back...
Aiming to solve the problems of high cost and large error, a prediction model of oil and gas pipeline corrosion rate was established by using a knowledge graph and neural network. The knowledge map of oil and gas pipeline corrosion was constructed by...
Graph neural networks have shown strong potential for learning structural representations of biological networks. However, repeated message passing may blur local structural signals that are relevant for motif- and graphlet-based analysis. This paper...
As key equipment in modern industry, it is important to diagnose and predict the health status of bearings. Data-driven methods for remaining useful life (RUL) prognostics have achieved excellent performance in recent years compared to traditional me...
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