Knowledge graph completion (KGC) addresses the issue of incomplete knowledge graphs by inferring missing triples, which is crucial for information retrieval, question answering, and recommender systems. Existing KGC methods generally focus on either exploiting the graph’s structural topology or leveraging the semantic information from entity descriptions. However, existing approaches often overlook the synergy between structure information and semantic information. To address the existing shortcomings, we proposeStrucSem, a novel model that leverages both structural and semantic information to boost KGC performance. Our model: 1) encodes the graph’s structural information by aggregating neighborhood data around the query entity using an attention mechanism; and 2) combines this with the encoding of textual descriptions, facilitating the integration of both types of information. Additionally, we extend contrastive learning to incorporate multiple positive samples, improving the model’s ability to represent diverse relational patterns. Our approach significantly enhances KGC performance, as demonstrated through extensive evaluations on standard benchmark datasets. The results highlight the superiority of combining structural and semantic information, offering new insights into improving KGC tasks.

Artificial intelligence techniques have been extensively employed in anomaly detection tasks for massive systems and equipment across numerous industries, achieving notable success. Nevertheless, classical machine learning methods typically possess a simplistic design, which occasionally fails to satisfy the detection demands of minor anomalies in certain complex tasks. Conversely, the interpretability of deep learning methods is often insufficient to convince domain experts and operators. Consequently, achieving a balance between detection accuracy and interpretability remains a critical and often conflicting challenge. This article proposes an expert experience-based algorithm unfolding (EAU) network and a conditional canonical correlation analysis (CCCA) theory for anomaly detection of spacecraft under multiple operating conditions, aiming to ensure detection accuracy while enhancing interpretability. First, the EAU network integrates the experiential knowledge with the Lasso regression model and employs sparse coding to iteratively expand it layer by layer (LbL), facilitating deep feature extraction from the original telemetry data. Second, the CCCA method formulates the residual vector on the premise that the correlation between the regularization components of the input and output sets will change markedly before and after the anomaly appears. It subsequently compares the HotellingT2statistic of each sample with the detection threshold, which was constructed based on the kernel density estimation (KDE) method, to discover the evolution of the anomaly. Finally, multigroup comparisons on two simulations and two real satellite-telemetry datasets verify the superior overall performance of the proposed method and provide guidance for selecting anomaly detection approaches.

Traditional video-to-audio generation techniques primarily focus on perspective video and non-spatial audio, often missing the spatial cues necessary for accurately representing sound sources in 3D environments. To address this limitation, we introduce a novel task, 360V2SA, to generate spatial audio from 360-degree videos, specifically producing First-order Ambisonics (FOA) audio - a standard format for representing 3D spatial audio that captures sound directionality and enables realistic 3D audio reproduction. We first create Sphere360, a novel dataset tailored for this task that is curated from real-world data. We also design an efficient semi-automated pipeline for collecting and cleaning paired video-audio data. To generate spatial audio from 360-degree video, we propose a novel framework OmniAudio, which leverages self-supervised pre-training using both spatial audio data (in FOA format) and large-scale non-spatial data. Furthermore, OmniAudio features a dual-branch framework that utilizes both panoramic and perspective video inputs to capture comprehensive local and global information from 360-degree videos. Experimental results demonstrate that OmniAudio achieves state-of-the-art performance across both objective and subjective metrics on Sphere360. Code and datasets are available at https://github.com/liuhuadai/OmniAudio. The project website is available at https://OmniAudio-360V2SA.github.io.

The increasing frequency and complexity of infectious disease outbreaks, exemplified by the COVID-19 pandemic, underscore the urgent need for accurate and adaptive epidemic forecasting models. Recently, spatio-temporal graph neural networks have shown potential in modeling infectious disease spread, as they effectively capture the interplay between spatial and temporal dependencies. However, existing STGNN-based approaches often treat disease transmission as a single, unified process, overlooking the distinct mechanisms underlying local and cross-regional spread. In this study, we propose D-STEM, a novel framework that decouples local and spillover transmission dynamics to achieve more precise predictions. Our approach integrates a hybrid local evolution module, combining GRU and self-attention mechanisms to model intra-regional transmission, and employs dynamic spatio-temporal convolution alongside population mobility networks to capture cross-regional transmission. Crucially, we introduce physical constraints to guide the disentanglement of these two mechanisms, ensuring that each module learns distinct features even in the absence of direct observations. Extensive experiments on real-world datasets, including US-state and Japan-prefecture COVID-19 data, demonstrate that D-STEM consistently outperforms all baselines. Our framework advances epidemic forecasting and provides actionable insights for public health interventions.

With the development of internet, transportation network, and other technologies, the transmission of information and disease presents complex and diverse new modes, which are mainly manifested as the coupling transmission of information and disease in the cyber–physical–social space. Inspired by this phenomenon, this article proposes a multilayer network-based information–behavior–disease coupling (IBDN) transmission model for the process of information diffusion–behavior change–disease transmission. The IBDN model considers various factors such as psychological drivers of information dissemination, the impact of herd mentality on behavioral transmission, the disease transmission dynamics of the current COVID-19 Omicron mutant strain and relevant countermeasures, and the interconnections between information, behavior, and disease transmission. Furthermore, within the framework of the COVID-19 Omicron mutant strain pandemic, the proposed IBDN model was leveraged to assess the effects of the propagation parameters of each layer and the interlayer coupling parameters on the magnitude of the COVID-19 outbreak and the strain on medical resources. A sensitivity analysis was carried out to determine the variability of the basic reproductive number of the Omicron mutant strains across various nations. Finally, the findings of the experiment were subjected to a thorough examination of policy implications to furnish valuable perspectives for the formulation of effective epidemic prevention strategies in the face of severe COVID-19 situation.

Decentralized trading schemes involving energy prosumers have prevailed in recent years. Such schemes provide a pathway for increased energy efficiency and can be enhanced by the use of blockchain technology to address security concerns in decentralized trading. To improve transaction security and privacy protection while ensuring desirable social governance, this article proposes a novel two-stage blockchain-based operation and trading mechanism to enhance energy hubs connected with integrated energy systems (IESs). This mechanism includes multienergy aggregators (MAGs) that use a consortium blockchain and its enabled proof-of-work (PoW) to transfer and audit transaction records, with social governance principles for guiding prosumers’ decision-making in the peer-to-peer (P2P) transaction management process. The uncertain nature of renewable generation and load demand are adequately modeled in the two-stage Wasserstein-based distributionally robust optimization (DRO). The practicality of the proposed mechanism is illustrated by several case studies that jointly show its ability to handle an increased renewable generation capacity, achieve a 16.7% saving in the audit cost, and facilitate 2.4% more P2P interactions. Overall, the proposed two-stage blockchain-based trading mechanism provides a practical trading scheme and can reduce redundant trading amounts by 6.5%, leading to a further reduction of the overall operation cost. Compared to the state-of-the-art benchmark methods, our mechanism exhibits significant operation cost reduction and ensures social governance and transaction security for IES and energy hubs.

As a crucial energy storage for the spacecraft power system, lithium-ion batteries degradation mechanisms are complex and involved with external environmental perturbations. Hence, effective remaining useful life (RUL) prediction and model reliability assessment confronts considerable obstacles. This article develops a new RUL prediction method for spacecraft lithium-ion batteries, where a hybrid data preprocessing-based deep learning model is proposed. First, to improve the correlation between battery capacity and features, the empirically selected high-dimensional features are linearized by using the Box-Cox transformation and then denoised via the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method. Second, the principal component analysis (PCA) algorithm is employed to perform feature dimensionality reduction, and the output of PCA is further processed by the sliding window technique. Third, a multiscale hierarchical attention bi-directional long short-term memory (MHA-BiLSTM) model is constructed to estimate the capacity in future cycles. Specifically, the MHA-BiLSTM model can predict the RUL of lithium-ion batteries by considering the correlation and significance of each cycle's information during the degradation process on different scales. Finally, the proposed method is validated based on multiple types of experiments under two lithium-ion battery datasets, demonstrating its superior performance in terms of feature extraction and multidimensional time series prediction.

The COVID-19 pandemic has had a global impact on communities, economies, and healthcare systems. To control the virus's spread, numerous epidemiological investigations have been made available online, leading to a growing demand for automated tools to extract valuable information from case reports and reduce the burden on news reporters. In response to this growing need, we have meticulously curated a comprehensive data set of COVID-19 epidemiological investigation corpora, specifically designed for named entity recognition (NER) applications. This data set enables researchers and analysts to efficiently identify and extract key information from the case reports, streamlining the process of understanding and communicating the findings. To further enhance the effectiveness of NER in the context of epidemiological investigations, we evaluated and compared the performance of three cutting-edge, pre-trained model-based methods: BERT-BiLSTM-CRF, ERNIE-BiLSTM- CRF and ALBERT-BiLSTM-CRF. All techniques demonstrated impressive performance in recognizing named entities within the case reports, showcasing their potential to revolutionize the way in which epidemiological data is analyzed and disseminated. By leveraging these advanced NER techniques, we aim to facilitate more accurate and timely reporting, ultimately contributing to better-informed decision-making processes and improved public health outcomes.

The rawly collected training data often comes with separate noisy labels collected from multiple imperfect annotators (e.g., via crowdsourcing). A typical way of using these separate labels is to first aggregate them into one and apply standard training methods. The literature has also studied extensively on effective aggregation approaches. This paper revisits this choice and aims to provide an answer to the question of whether one should aggregate separate noisy labels into single ones or use them separately as given. We theoretically analyze the performance of both approaches under the empirical risk minimization framework for a number of popular loss functions, including the ones designed specifically for the problem of learning with noisy labels. Our theorems conclude that label separation is preferred over label aggregation when the noise rates are high, or the number of labelers/annotations is insufficient. Extensive empirical results validate our conclusions.

As part of ongoing efforts to contain the coronavirus disease (COVID-19) pandemic, understanding the role of asymptomatic patients in the transmission system is essential for infection control. However, the optimal approach to risk assessment and management of asymptomatic cases remains unclear. This study proposed a Susceptible, Exposed, Infectious, No symptoms, Hospitalized and reported, Recovered, Death (SEINRHD) epidemic propagation model. The model was constructed based on epidemiological characteristics of COVID-19 in China and accounting for the heterogeneity of social contact networks. The early community outbreaks in Wuhan were reconstructed and fitted with the actual data. We used this model to assess epidemic control measures for asymptomatic cases in three dimensions. The impact of asymptomatic cases on epidemic propagation was examined based on the effective reproduction number, abnormally high transmission events, and type and structure of transmission. Management of asymptomatic cases can help flatten the infection curve. Tracing 75% of the asymptomatic cases corresponds to a 32.5% overall reduction in new cases (compared with tracing no asymptomatic cases). Regardless of population-wide measures, household transmission is higher than other types of transmission, accounting for an estimated 50% of all cases. The magnitude of tracing of asymptomatic cases is more important than the timing; when all symptomatic patients were traced, tested, and isolated in a timely manner, the overall epidemic was not sensitive to the time of implementing the measures to trace asymptomatic patients. Disease control and prevention within families should be emphasized during an epidemic.Note to Practitioners—This article addresses the urgent need to assess the risk of another COVID-19 outbreak caused by asymptomatic cases and to find the optimal, most practical approach to asymptomatic case management. Previous studies mostly focused on the clinical and statistical characteristics of asymptomatic cases; few have evaluated the impact of asymptomatic case measures using mathematical modeling at the community scale. This study proposed a Susceptible, Exposed, Infectious, No symptoms, Hospitalized and reported, Recovered, Death (SEINRHD) propagation model based on local community structures and social contact networks, according to the development characteristics and trend of COVID-19 in a Chinese community. The conclusion provides theoretical support for emergency work of relevant departments in different periods of an epidemic. In the early stages of the epidemic, timely detection and isolation of symptomatic patients should be a priority. Where there are surplus resources for epidemic prevention, the authorities should consider increasing the proportion of asymptomatic patients being traced. Epidemic prevention measures among family members should be a primary focus of attention. This combination of strategies can help reduce the rate of viral transmission and result in extinguishing the epidemic.

The 2019 Novel Coronavirus Disease (COVID-19) vaccines have been placed significant expectation to end the COVID-19 pandemic sooner. However, issues related to vaccines still need to be resolved urgently, including the vaccination number and range. In this paper, we proposed an epidemic spread model based on the hierarchical weighted network. This model fully considers the heterogeneity of the community social contact network and the epidemiological characteristics of COVID-19 in China, which enables to evaluate the potential impact of vaccine efficacy, vaccination schemes, and mixed interventions on the epidemic. The results show that a mass vaccination can effectively control the epidemic but cannot completely eliminate it. In the case of limited resources, giving vaccination priority to the individuals with high contact intensity in the community is necessary. Joint implementation with non-pharmacological interventions strengthening the control of virus transmission. The results provide insights for decision-makers with effective vaccination plans and prevention and control programs.

This paper studied the public opinion dissemination mechanism of public health events such as avian influenza on WeChat. We collected 25,572 posts related to “avian influenza” and “H7N9” from WeChat accounts and proposed the NRT model to simulate the spread of avian influenza public opinion in WeChat. Fitting results show that it can well explain the information dissemination process and mechanism within the WeChat public account. Then the influence of model parameters on the propagation of network public opinion is further studied. Our research can provide a theoretical basis for network public opinion prediction and prevention, and has great significance for the stability of the network environment.

ListNet is a well-known listwise learning to rank model and has gained much attention in recent years.A particular problem of ListNet, however, is the high computation complexity in model training, mainly due to the large number of object permutations involved in computing the gradients.This paper proposes a stochastic ListNet approach which computes the gradient within a bounded permutation subset.It significantly reduces the computation complexity of model training and allows extension to Top-k models, which is impossible with the conventional implementation based on full-set permutations.Meanwhile, the new approach utilizes partial ranking information of human labels, which helps improve model quality.Our experiments demonstrated that the stochastic ListNet method indeed leads to better ranking performance and speeds up the model training remarkably.