In volunteer computing (VC), the expected availability time and the actual availability time provided by volunteer nodes (VNs) are usually inconsistent. Scheduling tasks with precedence constraints in VC under this situation is a new challenge. In this paper, we propose two novel task assignment algorithms to minimize completion time (makespan) by a flexible task assignment. Firstly, this paper proposes a reliability model, which uses a simple fuzzy model to predict the time interval provided by a VN. This reliability model can reduce inconsistencies between the expected availability time and actual availability time. Secondly, based on the reliability model, this paper proposes an algorithm called EFTT (Earliest Finish Task based on Trust, EFTT), which can minimize makespan. However, EFTT may induce resource waste in task assignment. To make full use of computing resources and reduce task segmentation rate, an algorithm IEFTT (improved earliest finish task based on trust, IEFTT) is further proposed. Finally, experimental results verify the efficiency of the proposed algorithms.

Optimal maintenance decision for a sensor network aims to intelligently determine the optimal repair time. The accuracy of the optimal maintenance decision method directly affects the reliability and safety of the sensor network. This paper develops a new optimal maintenance decision method based on belief rule base considering attribute correlation (BRB-c), which is designed to address three challenges: the lack of observation data, complex system mechanisms, and characteristic correlation. This method consists of two sections: the health state assessment model and the health state prediction model. Firstly, the former is accomplished through a BRB-c-based health assessment model that considers characteristic correlation. Subsequently, based on the current health state, a Wiener process is used to predict the health state of the sensor network. After predicting the health state, experts are then required to establish the minimum threshold, which in turn determines the optimal maintenance time. To demonstrate the proposed method is effective, a case study for the wireless sensor network (WSN) of oil storage tank was conducted. The experimental data were collected from an actual storage tank sensor network in Hainan Province, China. The experimental results validate the accuracy of the developed optimal maintenance decision model, confirming its capability to efficiently predict the optimal maintenance time.

The widespread dissemination and societal impact of fake news underscore the critical need for effective detection. Existing methods remain limited, as they often fail to learn joint representations from multi-modal data and rely heavily on complete social interaction signals. Such information is frequently unavailable in practice, especially during the early propagation stages. To address early fake news detection in social media, this paper proposes a hierarchical cross-modal fusion framework with structured LLM-simulated social interaction (HCF-LSIM). The framework employs a progressive cross-modal attention mechanism to systematically align semantic representations across multiple levels, integrating textual, thematic, and visual features. Additionally, HCF-LSIM designs an LLM-powered social interaction simulator that generates structured triplets from adapted user profiles, effectively compensating for missing real-time interaction data. Experiments on public benchmarks demonstrate strong performance, with accuracies of 93.5% on Weibo and 87.2% on X (formerly Twitter), ranking first on Weibo and second on Twitter.