Blind image quality assessment (BIQA) methods often incorporate auxiliary tasks to improve performance. However, existing approaches face limitations due to insufficient integration and a lack of flexible uncertainty estimation, leading to suboptimal performance. To address these challenges, we propose a multitasks-based Deep Evidential Fusion Network (DEFNet) for BIQA, which performs multitask optimization with the assistance of scene and distortion type classification tasks. To achieve a more robust and reliable representation, we design a novel trustworthy information fusion strategy. It first combines diverse features and patterns across sub-regions to enhance information richness, and then performs local-global information fusion by balancing fine-grained details with coarse-grained context. Moreover, DEFNet exploits advanced uncertainty estimation technique inspired by evidential learning with the help of normal-inverse gamma distribution mixture. Extensive experiments on both synthetic and authentic distortion datasets demonstrate the effectiveness and robustness of the proposed framework. Additional evaluation and analysis are carried out to highlight its strong generalization capability and adaptability to previously unseen scenarios.

Recent advances in multimodal large language models (MLLMs) have significantly improved medical AI, enabling it to unify the understanding of visual and textual information. However, as medical knowledge continues to evolve, it is critical to allow these models to efficiently update outdated or incorrect information without retraining from scratch. Although textual knowledge editing has been widely studied, there is still a lack of systematic benchmarks for multimodal medical knowledge editing involving image and text modalities. To fill this gap, we present MedMKEB, the first comprehensive benchmark designed to evaluate the reliability, generality, locality, portability, and robustness of knowledge editing in medical multimodal large language models. MedMKEB is built on a high-quality medical visual question-answering dataset and enriched with carefully constructed editing tasks, including counterfactual correction, semantic generalization, knowledge transfer, and adversarial robustness. We incorporate human expert validation to ensure the accuracy and reliability of the benchmark. Extensive experiments on state-of-the-art general and medical MLLMs demonstrate the limitations of existing knowledge editing methods in the medical domain, highlighting the need to develop specialized editing strategies.

Molecular assembly (MA) has long been a fundamental task in chemistry and biology, with the potential to create new materials and enable novel functions beyond the molecular scale. However, its vast conformational search space poses substantial challenges, and current generative models remain limited in capturing molecular flexibility and preventing non-physical poses. In this paper, we propose AssemUDB, a diffusion bridge–based framework that learns transport mappings between two distinct flexible domains for molecular assembly generation. We reformulate the marginal matching constraint of diffusion bridges as a coupling distribution governed by unbalanced transport rather than imposing strict conservation. Subsequently, we employ a progressive process from structural relaxation in Euclidean space to assembly on the SE(3) manifold. This relaxation of marginal conservation grants the generative model greater flexibility and leads to more physically plausible atom placements. Comprehensive experiments demonstrate the superior performance of AssemUDB. Notably, we find that the method demonstrates performance comparable to, or even better than, mature tools such as PackMol for packing tasks.

Recent advancements in Multimodal Large Language Models (MLLMs) have demonstrated impressive capabilities in interpreting single medical images. However, real-world clinical diagnosis is intrinsically a multi-view process, requiring the synthesis of information across volumetric slices, temporal sequences, and comparative modalities. Existing benchmarks fail to capture this complexity, limiting the assessment of models in realistic clinical workflows. To bridge this gap, we introduce MedMultiBench, the first large-scale benchmark specifically designed for medical multi-image understanding. Comprising 11,392 expert-curated samples, MedMultiBench evaluates MLLMs across four distinct dimensions: Joint Reasoning, Comparative Analysis, Comprehensive Perception, and In-Context Learning. We benchmark 13 state-of-the-art MLLMs, revealing that while current models excel in single-view tasks, they struggle significantly with multi-image contexts. Our experiments identify a performance degradation in open-source models when processing increased visual loads, whereas closed-source models demonstrate better scalability. MedMultiBench provides a robust framework to facilitate the development of MLLMs capable of holistic clinical reasoning.

Online psychological counseling dialogue systems are trending, offering a convenient and accessible alternative to traditional in-person therapy. However, existing psychological counseling dialogue systems mainly focus on basic empathetic dialogue or QA with minimal professional knowledge and without goal guidance. In many real-world counseling scenarios, clients often seek multi-type help, such as diagnosis, consultation, therapy, console, and common questions, but existing dialogue systems struggle to combine different dialogue types naturally. In this paper, we identify this challenge as how to construct mixed-type dialogue systems for psychological counseling that enable clients to clarify their goals before proceeding with counseling. To mitigate the challenge, we collect a mixed-type counseling dialogues corpus termed STAMPsy, covering five dialogue types, task-oriented dialogue for diagnosis, knowledge-grounded dialogue, conversational recommendation, empathetic dialogue, and question answering, over 5,000 conversations. Moreover, spatiotemporal-aware knowledge enables systems to have world awareness and has been proven to affect one's mental health. Therefore, we link dialogues in STAMPsy to spatiotemporal state and propose a spatiotemporal-aware mixed-type psychological counseling dataset. Additionally, we build baselines on STAMPsy and develop an iterative self-feedback psychological dialogue generation framework, named Self-STAMPsy. Results indicate that clarifying dialogue goals in advance and utilizing spatiotemporal states are effective.

Coarse-grained (CG) molecular dynamics of proteins is a preferred approach to studying large molecules on extended time scales by condensing the entire atomic model into a limited number of pseudo-atoms and preserving the thermodynamic properties of the system. However, the significantly increased efficiency impedes the analysis of substantial physicochemical information, since high-resolution atomic details are sacrificed to accelerate simulation. In this paper, we propose LatCPB, a generative approach based on diffusion that enables high-resolution backmapping of CG proteins. Specifically, our model encodes an all-atom into discrete latent embeddings, aligned with learnable multimodal discrete priors for circumventing posterior collapse and maintaining the discrete properties of the protein sequence. During the generation, we further design a latent diffusion process within the continuous latent space due to the potential stochastics in the data. Moreover, LatCPB performs a contrastive learning strategy in latent space to separate feature representations of various molecules and conformations of the same molecule, thus enhancing the comprehension of molecular representational diversity. Experimental results demonstrate that LatCPB is able to backmap CG proteins effectively and achieve outstanding performance.

Currently, medical vision language models are widely used in medical vision question answering tasks. However, existing models are confronted with two issues: for input, the model only relies on text instructions and lacks direct understanding of visual clues in the image; for output, the model only gives text answers and lacks connection with key areas in the image. To address these issues, we propose a unified medical vision language model MIMO, with visual referring Multimodal Input and pixel grounding Multimodal Output. MIMO can not only combine visual clues and textual instructions to understand complex medical images and semantics, but can also ground medical terminologies in textual output within the image. To overcome the scarcity of relevant data in the medical field, we propose MIMOSeg, a comprehensive medical multimodal dataset including 895K samples. MIMOSeg is constructed from four different perspectives, covering basic instruction following and complex question answering with multimodal input and multimodal output. We conduct experiments on several downstream medical multimodal tasks. Extensive experimental results verify that MIMO can uniquely combine visual referring and pixel grounding capabilities, which are not available in previous models. Our project can be found in https://github.com/pkusixspace/MIMO.

Existing large language model (LLM) agents for automating data science show promise, but they remain constrained by narrow task scopes, limited generalization across tasks and models, and over-reliance on state-of-the-art (SOTA) LLMs.We introduce DatawiseAgent 1 , a notebook-centric LLM agent framework for adaptive and robust data science automation.Inspired by how human data scientists work in computational notebooks, DatawiseAgent introduces a unified interaction representation and a multi-stage architecture based on finitestate transducers (FSTs).This design enables flexible long-horizon planning, progressive solution development, and robust recovery from execution failures.Extensive experiments across diverse data science scenarios and models show that DatawiseAgent consistently achieves SOTA performance by surpassing strong baselines such as AutoGen and TaskWeaver, demonstrating superior effectiveness and adaptability.Further evaluations reveal graceful performance degradation under weaker or smaller models, underscoring the robustness and scalability.

Medical Visual Question Answering (Medical VQA) plays an important role in medical informatics. However, the robustness of existing medical VQA models is severely challenged by adversarial attacks. Current methods (e.g. adversarial training and noise-based reasoning) heavily rely on additional data or complex procedures and often ignore model-level robustness. To address these issues, we propose Multimodal Variational Masked Autoencoder (MVMAE), a novel pre-training framework designed to enhance the robustness of the medical VQA task. MVMAE leverages masked modeling and variational inference to extract robust multimodal features. The framework introduces a low-cost multimodal bottleneck fusion module and employs reparameterization to sample robust latent representations, ensuring effective feature fusion and reconstruction. Extensive experiments on public medical VQA datasets demonstrate that MVMAE significantly improves resistance to various adversarial attacks and outperforms other medical multimodal pre-training methods.

Molecular synthetic space projecting is a critical technique in de novo molecular design, which aims to rectify molecules without synthesizability guarantee by converting them into synthetic postfix notations. However, the vast synthesizable chemical space and the discrete data modalities involved pose significant challenges to postfix notation conversion benchmarking. In this paper, we exploit conditional probability transitions in discrete state space and introduce MoleBridge, a deep generative model built on the Markov bridge approach for designing postfix notations of molecular synthesis pathways. MoleBridge consists of two iterative optimizations: i) Autoregressive extending of notation tokens from molecular graphs, and ii) generation of discrete reaction postfix notations through Markov bridge, where noisy token blocks are progressively denoised over multi-step iterations. For the challenging second iteration, which demands sensitivity to incorrect generative probability paths within intricate chemical spaces, we employ a thinking and denoising separation approach to denoise. Empirically, we find that MoleBridge is capable of accurately predicting synthesis pathways while exhibiting excellent performance in a variety of application scenarios.

Disease is one of the primary factors affecting life activities, with complex etiologies often influenced by gene expression and mutation. Currently, wet lab experiments have analyzed the mechanisms of mutations, but these are usually limited by the costs of wet experiments and constraints in sample types and scales. Therefore, this paper constructs a real-world mutation-induced disease dataset and proposes Capsule and Graph topology networks with Multi-head attention (CGM) to predict the mutation-disease associations. CGM can accurately predict protein mutation-disease associations, and to further elucidate the pathogenicity of protein mutations, we also verified that protein mutations lead to protein structural alterations by the model, which suggests that mutation-induced conformational changes may be an important pathogenic factor. Limited by the size of the mutated protein dataset, we also performed experiments on benchmark and imbalanced datasets, where CGM mined 22 unknown protein interaction pairs from the benchmark dataset, better illustrating the potential of CGM in predicting mutation-disease associations. In summary, this paper curates a real dataset. It proposes that CGM predicts protein mutations and disease associations, providing a novel tool for further understanding of biomolecular pathways and disease mechanisms.

Complementarity determining region (CDR) is a specific region in antibody molecules that binds to antigens, where a small portion of residues undergoes particularly pronounced variations. Generating CDRs with high affinity and specificity is a pivotal milestone in accelerating drug development for daunting and unresolved diseases. However, existing approaches predominantly center on characterizing the attributes of residues through sequential generation models, thus falling short in effectively modeling the intricate spatial correlations among residues and frequently succumbing to the trap of generating sequences that exhibit a high degree of arbitrariness. In this article, we propose a novel synergistic attention-guided cascaded graph diffusion model, termed GraphCas, which offers a pathway for optimized generation of high-affinity CDRs. Our approach is the first cascaded-based graph diffusion model for CDR synthesis. Specifically, we design a graph propagation algorithm with a relation-aware synergistic attention mechanism, enabling the targeted acquisition of structural insights from diverse protein sequences and bolstering the global information representation of the graph by precisely localizing to long-range key residue sites. We design a cascaded conditional enhanced diffusion approach, providing the capability to incorporate additional control constraints into the input. Experimental results demonstrate that GraphCas can generate photo-realistic CDRs and achieve performance comparable to top-tier approaches. In particular, GraphCas reduces the RMSD by nearly 0.42 units in the H1 region and improves the ERRAT by 9.36% points in the L1 region.

The actual collection of tabular data for sharing involves confidentiality and privacy constraints, leaving the potential risks of machine learning for interventional data analysis unsafely averted. Synthetic data has emerged recently as a privacy-protecting solution to address this challenge. However, existing approaches regard discrete and continuous modal features as separate entities, thus falling short in properly capturing their inherent correlations. In this paper, we propose a novel contrastive learning guided Gaussian Transformer autoencoder, termed GTCoder, to synthesize photo-realistic multimodal tabular data for scientific research. Our approach introduces a transformer-based fusion module that seamlessly integrates multimodal features, permitting for mining more informative latent representations. The attention within the fusion module directs the integrated output features to focus on critical components that facilitate the task of generating latent embeddings. Moreover, we formulate a contrastive learning strategy to implicitly constrain the embeddings from discrete features in the latent feature space by encouraging the similar discrete feature distributions closer while pushing the dissimilar further away, in order to better enhance the representation of the latent embedding. Experimental results indicate that GTCoder is effective to generate photo-realistic synthetic data, with interactive interpretation of latent embedding, and performs favorably against some baselines on most real-world and simulated datasets.

Magnetic resonance (MR) image quality assessment plays a crucial role in disease diagnosis and data analysis. Existing methods typically treat the data as images and process them with convolutional networks, thereby ignoring the sequential characteristics of MR data. In this paper, we propose a hybrid spatial-frequency network (HSFNet) for MR image quality assessment, which extracts MR image quality features in both spatial and frequency domains. Specifically, within each data domain, information from local images and global sequences is iteratively integrated by applying a Mamba-based cascading processing module for multiple times. Extensive experiments on both T1-weighting and T2-weighting MR datasets demonstrate the proposed method’s effectiveness and generalization ability in comparison with state-of-the-art MR image quality assessment methods.

Functional connectivity (FC) graph analysis is an important method for diagnosing brain disorders using functional magnetic resonance imaging (fMRI). Existing FC graph diagnosis approaches preprocess the brain by dividing it into specific regions using atlases. However, relying on a single atlas exclusively for data preprocessing fails to fully harness the potential of the medical prior knowledge embedded within these atlases. To address this issue, this paper proposes a functional connectivity graph representation learning method that integrates multiple atlases and multiple views. This self-supervised approach to representation learning aligns features across different atlases through contrastive learning. Building upon this, we introduce an improved finite field- of-view multi-head attention mechanism for feature fusion. This mechanism is used to initialize a spectral graph convolutional network's nodes (GCN) with fusion features obtained through cross-atlas alignment. Meanwhile, edge initialization takes into account the inherent correlations among multiple independent atlas fusion features. The proposed method is validated on multi-site datasets and demonstrates superiority across various metrics. The experimental section provides visualizations of the learned feature alignment and consistency, evaluating the effectiveness of contrastive learning.

No-reference image quality assessment for magnetic resonance images (MRI) aims to generate quality evaluations closely aligned with human perception without the reliance on high-quality reference images. This faces challenges due to the limited availability of large-scale, publicly accessible datasets and the absence of rigorous scientific evaluation. To address these challenges, we underscore the importance of six quality indicators that are closely related to spatial and frequency domain features. Based on these indicators, three experts are employed to provide comprehensive quality ratings across eight public MRI datasets. In addition, we propose a novel approach to extract and combine quality feature representations from spatial and frequency domains. Within the spatial domain, we emphasize capturing anatomical details and structural integrity by applying two sets of data augmentation transformations, which produce data variants that facilitate spatial feature extraction via contrastive learning. Meanwhile, a parallel strategy is carried out in the frequency domain, focusing on signal variations and artifacts. Experimental results demonstrate the robustness of our approach in MRI quality assessment, effectively integrating insights from both spatial and frequency domain perspectives.

There is an urgent need to address the effective diagnosis of multiple diseases across various medical institutions while ensuring the privacy of medical data in IoT environments. This requires the model to have the ability of zero-shot generalization, which can not be satisfied by existing models. To address this issue, we propose a two-stage model for medical image diagnosis, based on decoupling and decorrelating. An adversarial architecture is built using a gradient reversal discriminator to improve the model’s robustness. To further address the mixed correlation within domain-invariant features achieved by disentanglement, we propose to mitigate feature dependency through sample weighting. The effectiveness of the model is validated using both the diabetic retinopathy and the skin lesion datasets. For cross-dataset experiment, we select two datasets for symmetric decoupling and reserve the remaining dataset as the test set. The test dataset is analogous to real-world scenarios, where all the samples and labels are completely unknown to the model. The experiments show that the model achieves excellent performance and outperforms baselines in most metrics, which demonstrate the effectiveness of our approach to address the issue of multi-center data privacy in IoT, with a focus on enhancing diagnostic accuracy while ensuring data security.

The automated generation of radiology reports has attracted significant attention in the field of bioinformatics. Currently, the main limitations of this task include insufficient utilization of prior medical knowledge, lack of efficient knowledge fusion algorithms, and less distinctiveness between different generated reports. To address these issues, we propose a novel algorithm for radiology report generation, which includes Structured Knowledge-Enhanced Multi-modal Attention (SKEMA) and Dual-Branch Contrastive Learning (DBCL) for the first time. SKEMA aims to effectively bridge the gap between visual and prior knowledge by leveraging the high-order adjacency matrix of the knowledge graph to weightedly fuse image features and knowledge features. We enhance both features through masking, and use the original features and augmented features as positive and negative samples in the dual-branch contrastive learning (DBCL). DBCL increases the differences between positive and negative samples to avoid generating templated results, and enhances the robustness of the model. Finally, we conducted experiments to demonstrate the effectiveness of our model on two public radiology datasets, IU-Xray and MIMIC-CXR. Our model outperformed previous baseline methods on both datasets and achieved excellent evaluation scores.

The inadequacy of labeled datasets for image quality assessment has led to the development and popularity of self-supervised approaches. However, most existing self-supervised methods primarily focus on content and fidelity features extracted with convolutional neural networks, overlooking the crucial importance of structural features in quality assessment. To address this problem, we present a novel self-supervised two-stream feature extraction and representation approach. In our approach, the first stream leverages a contrastive learning framework to extract image fidelity features, while the second stream emphasizes structural features by incorporating an attention mechanism. This innovative combination results in a comprehensive feature representation for quality assessment. Moreover, our proposed method facilitates transfer learning, allowing the pre-trained two-stream model in the source domain to be seamlessly applied to target domains for quality regression. This compatibility with transfer learning enhances the adaptability and generalization of the model. Extensive experiments are carried out on three synthetic distortion datasets to validate the effectiveness of our approach. The results demonstrate that our work not only competes with state-of-the-art self-supervised methods but also outperforms some supervised approaches.

Physically Unclonable Functions (PUFs), as an alternative hardware-based security method, have been challenged by some modeling attacks. As is known to all, samples are significant in modeling attacks on PUFs, and thus, some efforts have been made to expand sample sets therein to improve modeling attacks. A closer examination, however, reveals that not all samples contribute to modeling attacks equally. Therefore, in this article, we introduce the concept of sample essentiality for describing the contribution of a sample in modeling attacks and point out that any sample without sample essentiality cannot enhance some modeling attacks on PUFs. As a by-product, we find theoretically and empirically that the samples expanded by the procedures proposed by Chatterjee et al. do not satisfy our sample essentiality. Furthermore, we propose the notion of essential sample sets for datasets and discuss its basic properties. Finally, we demonstrate that our results about sample essentiality can be used to reduce samples efficiently and benefit sample selection in modeling attacks on arbiter PUFs.

Modern power grid can produce a large amount of data at run time which shows a feature of fragmentation and disordering. Using the method of cloud-based knowledge management to achieve grid data, information retrieval, situation deducing and disaster warning, are important thoughts to be implemented. To solve the challenge, we put forward a software framework including knowledge recommendation and situation inference based on cloud computing and CEP(Complex Event Process). The framework can realize Large-scale analysis and intelligent recommendation for power grid and build reduction rules and models of power grid accident to implement disaster warning through CEP. Also, we show the prototype system.

With the development of science and technology, simulation technology has been widely applied in all fields, such as complexity of model design field. And it has become an indispensable supportive technology, but the current simulation model is still lack of a formal description method for model verification and the analysis. Based on Petri nets, this paper presents a data-driven simulation model and the formal definition, then describes the model on the basis of the operation and shows an example to describe the principle and process of the formal model. A simulation model definition language that extends the PNML language is proposed in this paper and XML Schema is used for implementation.

Cloud computing is a recently developed new technology for complex systems with massive service sharing, which is different from the resource sharing of the grid computing systems. In a cloud environment, service requests from users go through numerous provider-specific steps from the instant it is submitted to when the requested service is fully delivered. Quality modeling and analysis of clouds are not easy tasks because of the complexity of the automated provisioning mechanism and dynamically changing cloud environment. This work proposes an analytical model-based approach for quality evaluation of Infrastructure-as-a-Service cloud by considering expected request completion time, rejection probability, and system overhead rate as key quality metrics. It also features with the modeling of different warm-up and cool-down strategies of machines and the ability to identify the optimal balance between system overhead and performance. To validate the correctness of the proposed model, we obtain simulative quality-of-service (QoS) data and conduct a confidence interval analysis. The result can be used to help design and optimize industrial cloud computing systems.

Abstract Quality of service (QoS) of workflows and workflow‐based applications is given increasing attention by both industry and academic. In this paper, we propose a novel analytical framework to analyze QoS (metrics include make‐span, cost, and reliability) of workflow systems based on GWF‐net, which extends traditional workflow net by associating tasks with generally distributed firing delay and time‐to‐failure. The GFW‐net model is used to model process structure and task organization of workflows at the process level. In contrast with prevailing QoS models based on Markovian process, we introduce a reduction technique to evaluate QoS of GWF‐net process avoiding the state‐explosion problem and tedious mathematical derivation of state‐transition probabilities. Through a case study, we show that our framework is capable of modeling real‐world workflow‐based application effectively. Also, experiments and confidence‐interval analysis in the case study indicate that the reduction methods are verified by real results. We also compare our approach with related research in the text. Copyright © 2008 John Wiley & Sons, Ltd.

Web service composition involves the combination of a number of existing web services to create a value-added one. WS-BPEL is a promising language which describes Web service composition in form of business processes. However, WS-BPEL is an XML-based language and may suffer from ambiguities or some erroneous properties. The analysis and verification of business processes specified in WS-BPEL by a formal method has been a hot topic in the research community lately. In this paper, we propose a method to model and analyze WS-BPEL business processes based on ServiceNet, a special class of Petri nets. Unlike most of the existing work which analyze only control flow properties of WS-BPEL business processes but neglect data flow properties, our method models and analyzes both control properties and data properties of WS-BPEL business processes. We present the transformation rules of WS-BPEL business processes into ServiceNet and enrich the reduction rules of ServiceNet by applying them in some practical projects. Then the throughness of a business processes can be verified by reducing its ServiceNet representation based on some reduction rules. Moreover, we define some data-aspect properties of WS-BPEL business processes and give the corresponding checking algorithm.

At present, workflow technology has become a research hotspot in computer application domain, one of its major theoretical research areas is to build a formal model with which process definition, process execution and process analysis are well supported. Workflow process logic (workflow logic) specifies the causality among activities of process model. By dissecting common workflow patterns, synchronic distance of Petri net synchronic theory is used to specify workflow logic. As a start, the background of this paper, i.e. P/T system based hierarchical formal workflow model - procedure net, is briefly introduced, workflow logic is the basic layer of the hierarchical model. Then, the fact that synchronic distance has enough capability to specify workflow logic is proved, and the specification of well-defined workflow logic is given. Finally, Petri net representation of workflow logic is proposed based on the works above.

Abstract The analysis of workflow is crucial to the correctness of workflow applications. This paper introduces a simple and practical method for analyzing workflow logic models. Firstly, some definitions of the models and some properties, such as throughness, no‐redundant‐transition and boundedness, are presented. Then, we propose an approach based on synchronized reachability graphs (SRGs) to verify these properties. The SRG uses the characteristics of synchronizers in workflow logic models and mitigates the state explosion by constructing synchronized occurrence sequences rather than interleaving occurrence sequences. This paper also proposes some refined and feasible reduction rules which can preserve vital properties of workflow logic models. Using these two techniques, the SRG‐based verification method can achieve higher efficiency. Furthermore, this research also develops a verification tool based on the method, presents the analysis results of some practical cases and compares our method with others. Copyright © 2007 John Wiley & Sons, Ltd.

Abstract There is a growing demand for using commercial‐off‐the‐shelf (COTS) software components to facilitate the development of software systems. Among many research topics for component‐based software, quality‐of‐service (QoS) evaluation is yet to be given the importance it deserves. In this paper, we propose a novel analytical model to evaluate the QoS of component‐based software systems. We use the component execution graph (CEG) graph model to model the architecture at the process level and the interdependence among components. The CEG graph can explicitly capture sequential, parallel, selective and iterative compositions of components. For QoS estimation, each component in the CEG model is associated with execution rate, failure rate and cost per unit time. Three metrics of the QoS are considered and analytically calculated, namely make‐span, reliability and cost. Through a case study, we show that our model is capable of modeling real‐world COTS software systems effectively. Also, Monte‐Carlo simulation in the case study indicates that analytical results are consistent with simulation and all are covered by 95% confidence intervals. We also present a sensitivity analysis technique to identify QoS bottlenecks. This paper concludes with a comparison with related work. Copyright © 2007 John Wiley & Sons, Ltd.

Performance prediction is one of the most important research topics of workflow. To investigate the performance of workflow systems in queuing condition, this paper extends traditional WF-net into WFQN (WF queuing network), by modeling tasks as FIFS (first-in-first-service) queues and the source place as the input of tokens following poisson arrival process. Analytical methods are introduced to evaluate the queue-length, wait-time and completion-duration. The case study (especially the case of airline ticket booking application) shows that WFQN can model real-world workflow-based applications effectively. Through Montecarlo simulations in the case study, we show analytical models are verified by simulative results. We also present a sensitivity analysis technique to identify performance bottle-necks of WFQN. This paper concludes with a comparison with relate work.