Multi-agent debate (MAD) frameworks have emerged as promising approaches for misinformation detection by simulating adversarial reasoning. While prior work has focused on detection accuracy, the importance of helping users understand the reasoning behind factual judgments has been overlooked. The debate transcripts generated during MAD offer a rich but underutilized resource for transparent reasoning. In this study, we introduce ED2D, an evidence-based MAD framework that extends previous approach by incorporating factual evidence retrieval. More importantly, ED2D is designed not only as a detection framework but also as a persuasive multi-agent system aimed at correcting user beliefs and discouraging misinformation sharing. We compare the persuasive effects of ED2D-generated debunking transcripts with those authored by human experts. Results demonstrate that ED2D outperforms existing baselines across three misinformation detection benchmarks. When ED2D generates correct predictions, its debunking transcripts exhibit persuasive effects comparable to those of human experts; However, when ED2D misclassifies, its accompanying explanations may inadvertently reinforce users’ misconceptions, even when presented alongside accurate human explanations. Our findings highlight both the promise and the potential risks of deploying MAD systems for misinformation intervention. We further develop a public community website to help users explore ED2D, fostering transparency, critical thinking, and collaborative fact-checking.

Moving target selection in multimedia interactive systems faces unprecedented challenges as users increasingly interact across diverse, dynamic contexts-from live streaming in moving vehicles to VR gaming in varying environments. Existing approaches rely on probabilistic models that relate endpoint distribution to target properties (size, speed). However, these methods require substantial training data for each new context and lack transferability across scenarios, limiting their practical deployment in diverse multimedia environments where rich multimodal contextual information is readily available. This paper introduces MAGNeT (Multimodal Adaptive Gaussian Networks), which addresses these problems by combining classical statistical modeling with context-aware multimodal method. MAGNeT dynamically fuses pre-fitted Ternary-Gaussian models from various scenarios based on real-time contextual cues, enabling effective adaptation with minimal training data while preserving model interpretability. We take experiments on self-constructed 2D and 3D moving target selection datasets under in-vehicle vibration conditions. Extensive experiments demonstrate that MAGNeT achieves lower error rates with few-shot samples, by applying context-aware fusion of Gaussian experts from multi-factor conditions.

Integrating wireless energy transfer (WET) and data gathering based on the mobile platforms, such as the unmanned aerial vehicle (UAV) has been recognized as a promising technique to prolong the battery lifetime of resource-constrained wireless sensors in the Internet of Things era. However, it is challenging to jointly schedule dynamic renewable energy sources and communications resources to coordinate heterogeneous performance requirements in rechargeable wireless sensor networks (RWSNs). Hence, this article researches rechargeable adaptive energy-efficient dual-mode data gathering (AED2G) using renewable energy sources. First, considering the limited endurance of UAV and the uncertainty of renewable energy harvesting, a life-expectancy-balance-based AED2G strategy is proposed for optimizing the communication energy efficiency of the fixed data gathering (FDG) and mobile data gathering (MDG). Then, considering WET and MDG, the synergy payoff function of rechargeable MDG (RMDG) is designed, and the corresponding synergy payoff maximization problem is established. The problem is nonconvex due to the coupling of MDG and WET, so it is decomposed into two layers to be quickly solved by the designed hierarchical decomposition framework. The simulation results prove that our algorithm can efficiently use renewable energy sources, whether in FDG or RMDG mode, thereby improving the sustainability of RWSN.

In recent years, wireless smart sensors powered by solar energy have been widely deployed to ensure the green and sustainable operation of remote industrial systems monitoring. Such devices can offload computing tasks locally or using the edge server by transmitting the raw data wirelessly. Since random renewable energy harvesting has a detrimental effect on the energy balance of nodes in these green rechargeable wireless sensor networks (RWSN), the rational synergy between terminal-edge collaborative tasks offloading (TECTO) and network topology optimization (NTO) is of great significance for improving the sustainability. Therefore, this article presents a learning-based terminal-edge collaborative energy-efficient routing algorithm. First, a system model is developed to integrate TECTO and NTO, and the original problem is decoupled into two layers. Then, the NTO layer is aimed at quickly generating an energy-efficient network topology by variable cycle block coordinate descent method based on the greedy strategy. Finally, the TECTO layer adopts deep reinforcement learning based on the dynamic baseline to understand the energy efficiency feedback law of the NTO layer and rationally adjusts the TECTO scheme. The simulation results show that the presented algorithm can reasonably generate the network topology and TECTO scheme according to the node's energy state change and efficiently consume the renewable energy distributed in the green RWSN, which significantly enhances its sustainability.