With the increasing relevance of web mining and content analysis in uncovering mobility patterns from large-scale online data, traffic flow prediction plays a crucial role in proactive urban planning and enhancing the responsiveness of intelligent transportation systems. However, existing traffic flow prediction methods often fail to explicitly capture correlations in continuous multivariate sequences that are naturally suited for trend and periodic pattern extraction by vision models and rely on fixed spatial graphs that neglect the cognitive advantages of granular-ball structures, which limits their ability to model interactions and strengthen spatiotemporal dependencies. To address these challenges, this paper proposes a Cross-domain Mamba framework that integrates Multi-scale Imaging and Granular-ball Computing for traffic flow prediction (MIGC-CMamba). First, a multi-scale sequence imaging method is presented, which converts the original time series into image modality and leverages MambaVision to capture both local and global dependencies. Second, a multi-granularity spatial graph is constructed via granular-ball clustering, which balances global trend representation and local detail preservation. Third, a cross-domain enhancement mechanism adaptively integrates temporal and spatial domains, strengthening spatiotemporal dependencies. Lastly, extensive experiments demonstrate superior performance over state-of-the-art baselines, highlighting how vision-based imaging, cognition-inspired granular-ball modeling, and content-aware mining jointly advance the modeling of spatiotemporal dependencies in traffic flow prediction.

Metro flow prediction is a critical application in smart city and Web of Things infrastructures, essential for optimizing urban mobility. However, building such predictive systems faces three key challenges: (1) the fragmentation of multimodal spatiotemporal data, (2) the inefficiency of existing models in capturing long-range dependencies, and (3) the data silos and privacy concerns inherent in distributed station infrastructures. To address these challenges, a multimodal federated learning framework named MF3 (Mamba-Transformer-Federated Metro Flow Prediction) is proposed. First, a multimodal alignment (MA) module is designed, where cross-modal alignment attention bridges visual and spatiotemporal features, thereby enhancing feature complementarity and alignment. Second, a dual-path Mamba-Transformer (DMT) module is designed, in which Mamba's linear long-range memory and the Transformer's global perception operate in parallel, reducing information loss. Third, a blockchain-based federated reputation (BFR) module is established to perform personalized federated learning, thereby enhancing privacy protection. Finally, extensive experiments on real metro datasets from Hangzhou and Shanghai demonstrate that MF3 achieves superior performance in terms of prediction accuracy. In summary, the proposed MF3 framework provides a new feasible paradigm for metro flow prediction, supporting urban traffic optimization, metro operation and scheduling, and the development of smart city and Web of Things infrastructures.

With the continuous advancement of data processing technologies, decision-making problems based on multi-modal signal systems (MMSSs) can be effectively addressed. In this context, rebalancing problems in dockless bike-sharing systems (DBSSs) within social networks increasingly rely on MMSSs. However, MMSSs introduce the issue of declining information quality, making it essential to ensure the reliability of both signals and models. Specifically, the paper addresses these challenges by exploring methods to reduce the impact of low-quality signals in MMSSs, leading to the design of a trustworthy multi-modal signal processing (TMSP) model. Yet, two main challenges remain in building the model, i.e., the lack of suitable frameworks to accurately characterize MMSSs, and insufficient coupling between clustering analysis and the consensus-reaching process (CRP). To address these challenges, the signal reliability is first improved by processing MMSSs with complex intuitionistic fuzzy sets (CIFSs). Then, based on the MMSS, a trustworthy clustering and two-stage, two-index CRP model based on similarity measurement (T-CT2CRP-SM) is constructed. Subsequently, experiments select a real-world MMSS-based DBSS rebalancing problem as the scenario and illustrate detailed decision-making steps. Finally, multiple experimental analyses are conducted to further validate the reliability and stability of the constructed model.

Event causality identification (ECI) aims to detect causal relationships between events in textual contexts. Existing ECI models predominantly rely on supervised methodologies, suffering from dependence on large-scale annotated data. Although large language models (LLMs) enable zero-shot ECI, they are prone to causal hallucination—erroneously establishing spurious causal links. To address these challenges, we propose MEFA, a novel zero-shot ECI model based on multisource evidence fuzzy aggregation. First, we decompose causality reasoning into three main tasks (temporality determination, necessity analysis, and sufficiency verification) complemented by three auxiliary tasks. Second, leveraging meticulously designed prompts, we guide LLMs to generate uncertain responses and deterministic outputs. Finally, we quantify LLM's responses of subtasks and employ fuzzy aggregation to integrate these evidence for causality scoring and causality determination. Extensive experiments on three benchmarks demonstrate that MEFA outperforms second-best unsupervised baselines by 6.2% in$F1$-score and 9.3% in precision, while significantly reducing hallucination-induced errors. In-depth analysis verify the effectiveness of task decomposition and the superiority of fuzzy aggregation.

Classical clustering algorithms such as k-means face limitations in handling clusters with heterogeneous shapes, densities, and sizes, while exhibiting sensitivity to initial centroid selection. To overcome these challenges, this article proposes a novel clustering framework based on regenerated granular ball (RGGB) with the principle of justifiable granularity. Unlike existing granular-ball (GB) techniques that overemphasize purity criteria at the expense of uncontrolled ball sizes, RGGB dynamically adjusts granularity levels through iterative regeneration, achieving an optimal balance between detailed data representation and computational efficiency. This adaptability enhances stability in capturing data similarities while mitigating sensitivity to initialization. To validate the method, we integrate RGGB with a novel k-nearest neighbor (KNN) classifier using regenerated GBs to evaluate classification performance and demonstrate practical applications. Experiments on diverse public and realistic datasets demonstrate that the RGGB-based KNN algorithm consistently outperforms existing techniques, including traditional KNN and other methods, making a promising advancement in clustering and classification tasks.

This article investigates the adaptive sliding mode control (ASMC) for a class of nonlinear impulsive hybrid systems with time-varying delay, adopting a creative approach that emphasizes the switching perspective. When both impulse and sliding mode control are involved in the time-delay system, ensuring the continuity of the sliding mode function (SMF) and achieving the reachability of system states become crucial challenges that need to be addressed. In view of this, a novel impulse-based SMF is developed such that the impulsive effect can be avoided on sliding surface, and the difficulty of its continuity is also settled at impulsive instants. By using the state augmented approach, the delay-dependent Lyapunov function with switched systems is formulated to guarantee robust stability of the given sliding mode dynamics. Meanwhile, the designed ASMC law is derived to achieve the finite reachability of switching surface for system states. It is shown that the proposed ASMC law offers a high degree of freedom for adjusting the constants in nonlinear assumptions. Finally, comparative studies are conducted to validate the theoretical results and demonstrate their practical applicability.

Fuzzy C-means (FCM) is a clustering algorithm based on partition of the universe. However, the partition generated by an equivalence relation is strict in practical application and exhibits relatively poor fault-tolerant mechanism. In this article, a novel binary relation based on improved FCM with the principle of refined justifiable granularity is presented. Different expressions of the proposed binary relation under different values of weight parameter are discussed, and the changes of the properties of the binary relation under different parameter values are provided. By measuring the significance of attributes in the feature space, a feature selection method, called forward heuristic feature selection (FHFS), is designed to construct the low-dimension feature space based on maximizing the original data and information retention through the defined degrees of aggregation and dispersion. It is shown how the results of feature selection and classification performance vary when the values of the weight factor locate in different ranges. To illustrate the superiority and effectiveness of the proposed FHFS algorithm, nine high-dimensional datasets and eight image datasets from University of California-Irvine (UCI) repository are used and compared with other feature selection methods, respectively. The results of experimental evaluation and the significance test show that the proposed learning mechanism is a superior algorithm.

Dominance-based rough approximation discovers inconsistencies from ordered criteria and satisfies the requirement of the dominance principle between single-valued domains of condition attributes and decision classes. When the ordered decision system (ODS) is no longer single-valued, how to utilize the dominance principle to deal with multivalued ordered data is a promising research direction, and it is the most challenging step to design a feature selection algorithm in interval-valued ODS (IV-ODS). In this article, we first present novel thresholds of interval dominance degree (IDD) and interval overlap degree (IOD) between interval values to make the dominance principle applicable to an IV-ODS, and then, the interval-valued dominance relation in the IV-ODS is constructed by utilizing the above two developed parameters. Based on the proposed interval-valued dominance relation, the interval-valued dominance-based rough set approach (IV-DRSA) and their corresponding properties are investigated. Moreover, the interval dominance-based feature selection rules based on IV-DRSA are provided, and the relevant algorithms for deriving the interval-valued dominance relation and the feature selection methods are established in IV-ODS. To illustrate the effectiveness of the parameters variation on feature selection rules, experimental evaluation is performed using 12 datasets coming from the University of California-Irvine (UCI) repository.

Most exiting results for impulsive switched systems (ISSs) are mainly built on the synchronous switching and impulses case; however, the impulses can not only occur in switched interval including switched instants but also the switched signals may exist between two impulsive points in practical instants. Under asynchronous impulses and switching signals, the main objective of this article is to study the exponential stability of fractional-order hybrid systems. In order to better characterize stability, some novel criteria are presented by adopting the mode-dependent average impulsive interval and induction method. The obtained impulsive switched criteria lead to a tradeoff between fractional-order α and impulsive strength. Especially, the impulsive effects (positive or negative) with the order α are also discussed in detail, which extends the previous integer order results. Moreover, numerical examples are given to interpret and verify the effectiveness of the obtained criteria.

The main task of granular computing (GrC) is about representing, constructing, and processing information granules. Information granules are formalized in many different approaches. Different formal approaches emphasize the same fundamental facet in different ways. In this paper, we propose a novel GrC method of machine learning by using formal concept description of information granules. Based on information granules, the model and mechanism of two-way learning system is constructed in fuzzy datasets. It is addressed about how to train arbitrary fuzzy information granules to become necessary, sufficient, and necessary and sufficient fuzzy information granules. Moreover, an algorithm of the presented approach is established, and the complexity of the algorithm is analyzed carefully. Finally, to interpret and help understand the theories and algorithm, a real-life case study is considered and experimental evaluation is performed by five datasets from the University of California-Irvine, which is valuable for applying these theories to deal with practical issues.

The decision-theoretic rough set model based on Bayesian decision theory is a main development tendency in the research of rough sets. To extend the theory of decision-theoretic rough set, the article devotes this study to presenting multigranulation decision-theoretic rough set model in ordered information systems. This new multigranulation decision-theoretic rough set approach is characterized by introducing the basic set assignment function in an ordered information system. It is addressed about how to construct probabilistic rough set and multigranulation decision-theoretic rough set models in an ordered information system. Moreover, three kinds of multigranulation decision-theoretic rough set model are analyzed carefully in an ordered information system. In order to explain probabilistic rough set model and multigranulation decision-theoretic rough set models in an ordered information system, an illustrative example is considered, which is helpful for applying these theories to deal with practical issues.