Predicting the dynamics of infectious diseases plays an important role in their prevention and control. However, traditional epidemic models, such as susceptible-infected-recovered (SIR), susceptible-exposed-infected-recovered (SEIR), and their variants, are often inadequate for accurately capturing behavior-driven and heterogeneous transmission at scale. In this study, we propose a nonlinear dynamic model-the susceptible-low-susceptible-exposed-infected-recovered-death (SLEIRD) model-that further extends the SEIR framework. SLEIRD explicitly differentiates susceptible populations by introducing a low-susceptible group adopting protective measures, models four infection channels from susceptible/low-susceptible to exposed/infectious groups, thereby providing a behavior-aware and structurally richer compartmentalization than classical models. To validate its effectiveness, we compare SLEIRD with other classical epidemic models using real-world epidemic data, predicting confirmed cases and deaths over five, ten, fifteen, twenty, and thirty days. Experimental results show that SLEIRD outperforms SIR, improving the root mean squared error (RMSE) for daily confirmed cases and deaths by 6.0%-46.3% and 31.1%-62.3%, respectively; compared with SEIR, SLEIRD reduces the average RMSE for daily confirmed cases and deaths by 6.8%-56.6% and 59.8%-79.6%, respectively. These findings indicate that SLEIRD more accurately simulates complex spread dynamics and offers a scalable, behavior-aware tool for data-driven epidemic forecasting with practical value for disease control and prevention.

Abstract Link prediction aims at predicting the missing links or new links based on known topological or attribute information of networks, which is one of the most significant and challenging tasks in complex network analysis. Recently, many local similarity-based methods have been proposed and they performed well in most cases. However, most of these methods simultaneously ignore the contributions of the local structure information between endpoints and their common neighbors, as well as transmission abilities of different 3-hop paths. To address these issues, in this paper, we propose a novel link prediction method that aims at improving the prediction accuracy of the existing local similarity-based methods by integrating with local structure information and node degree information along 3-hop paths. Extensive experiments have been performed on nine real-world networks and the results demonstrate that our proposed method is superior to the existing state-of-the-art methods.

MOTIVATION: Extracting useful molecular features is essential for molecular property prediction. Atom-level representation is a common representation of molecules, ignoring the sub-structure or branch information of molecules to some extent; however, it is vice versa for the substring-level representation. Both atom-level and substring-level representations may lose the neighborhood or spatial information of molecules. While molecular graph representation aggregating the neighborhood information of a molecule has a weak ability in expressing the chiral molecules or symmetrical structure. In this article, we aim to make use of the advantages of representations in different granularities simultaneously for molecular property prediction. To this end, we propose a fusion model named MultiGran-SMILES, which integrates the molecular features of atoms, sub-structures and graphs from the input. Compared with the single granularity representation of molecules, our method leverages the advantages of various granularity representations simultaneously and adjusts the contribution of each type of representation adaptively for molecular property prediction. RESULTS: The experimental results show that our MultiGran-SMILES method achieves state-of-the-art performance on BBBP, LogP, HIV and ClinTox datasets. For the BACE, FDA and Tox21 datasets, the results are comparable with the state-of-the-art models. Moreover, the experimental results show that the gains of our proposed method are bigger for the molecules with obvious functional groups or branches. AVAILABILITY AND IMPLEMENTATION: The code and data underlying this work are available on GitHub at https://github. com/Jiangjing0122/MultiGran. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Minimax similarity stresses the connectedness of points via mediating elements rather than favoring high mutual similarity. The grouping principle yields superior clustering results when mining arbitrarily-shaped clusters in data. However, it is not robust against noises and outliers in the data. There are two main problems with the grouping principle: first, a single object that is far away from all other objects defines a separate cluster, and second, two connected clusters would be regarded as two parts of one cluster. In order to solve such problems, we propose robust minimum spanning tree (MST)-based clustering algorithm in this letter. First, we separate the connected objects by applying a density-based coarsening phase, resulting in a low-rank matrix in which the element denotes the supernode by combining a set of nodes. Then a greedy method is presented to partition those supernodes through working on the low-rank matrix. Instead of removing the longest edges from MST, our algorithm groups the data set based on the minimax similarity. Finally, the assignment of all data points can be achieved through their corresponding supernodes. Experimental results on many synthetic and real-world data sets show that our algorithm consistently outperforms compared clustering algorithms.

Traditional virtual screening in the grid needs chemists to upload small molecule files and collect the results manually, which cannot implement docking and collection of results automatically. This caused heavy workload to chemists. In this paper, we took advantage of Hadoop platform in the massive data storage. We stored and managed small molecule files and docking results files using HDFS. In addition, MapReduce programming framework is used for parallel molecular docking to preliminarily process results files, in order to achieve the automation of the virtual screening molecular docking. The research of this thesis will be helpful to drug researcher by offering a massive data storage management system for large-scale virtual screening, and will also provide a reference for drug discovery in the cloud environment to promote the development of computational chemistry e-science.

SWF, short for scientific workflow, has recently emerged as a paradigm for orchestrating large-scale e-Science applications. SWF specification connects workflow designer with workflow engine, which makes it act ass one of the key components in SWF systems. We adopted BPEL as the Gird-based SWF specification because of its potential benefits to promote SWF sharing and reproducibility. However, grid-based SWF has unique features, which pure BPEL is not capable to handle. Appropriate ways to extend BPEL to fulfill the special needs should be found. We concluded three most necessary requirements and three kinds of alternative methods to extend BPEL within China Grid Support Platform, and implemented the method of adding additional abstractions upon BPEL to enhance user experience. Our work and findings suggest that our extension approach is feasible and would manifest potential advantage to SWF systems and finally innovate in the e-Science research and applications.

In this paper, we study the impact-of bandwidth-delay products and non-responsive flows on queue management schemes. Our focus is to understand the aggregate performance of various classes of traffic under different queue management schemes. Our work is motivated by the expected trends of increasing link capacities and increasing amounts of non-responsive traffic. In this paper, the impact on the performance of RED, RED with ECN enabled and droptail routers are investigated. Our study considers the aggregate bandwidth of different classes of traffic and the delays observed at the router.

In this paper, a scheme for estimating the proportion of the incoming traffic that is not responsive to congestion at a router is presented. The idea of the proposed scheme is that if the observed queue length and packet drop probability do not match the predictions from a model of responsive (TCP) traffic, then the error must come from nonresponsive traffic; it can then be used for estimating the proportion of nonresponsive traffic. The proposed scheme is based on the queue length history, packet drop history, and expected TCP and queue dynamics. The effectiveness of the proposed scheme over a wide range of traffic scenarios is corroborated using ns-2-based simulations. Potential applications of the proposed algorithms in traffic engineering and control are discussed.

In this paper, we propose a scheme for estimating the proportion of the incoming traffic that is not responding to congestion at a router. The idea of the proposed scheme is that if the observed queue length and packet drop probability do not match with the predicted results from the TCP model, then the error must come from the non-responsive traffic; it can then be used for estimating non-responsive traffic. The proposed scheme utilizes queue length history, packet drop history, expected TCP and queue dynamics to estimate the proportion. We show that the proposed scheme is effective over a wide range of traffic scenarios through simulations.