As a key component of a three-phase inverter, the insulated gate bipolar transistor (IGBT) switching device plays a critical role in ensuring system stability, making its state monitoring highly important. However, due to the complex structure of three-phase inverters, many detection methods are unable to monitor the health status of these critical components in real time. To address this issue, this paper proposes an online monitoring method based on a convolutional neural network (CNN) algorithm. This method monitors the status of key components in real time by analyzing output parameters, thereby reducing the number of required sensors and simplifying the monitoring process. The proposed method has been validated through simulation experiments on a three-phase inverter, and the corresponding experimental results are presented.

LLM-based empathetic dialogue systems enhance agents' emotional support capabilities. Previous approaches primarily relied on Chain-of-Thought (CoT) prompting to extract key dialogue cues and further strengthened the agent's sensitivity to these signals through supervised fine-tuning. However, such methods overly depend on the information extraction capability of LLMs, leading to unstable reasoning and limited interpretability. To simultaneously improve an agent's ability to proactively explore solutions through rational reasoning while attending to users' sensitive emotions via empathetic understanding, we propose Neuro-Sym Supporter, a hybrid decision-making emotional support agent that integrates symbolic reasoning with deep learning. This model combines rational inference with emotional empathy, enabling the agent to generate supportive responses that balance logic and emotion. Specifically, we introduce Sym-Mind, a differentiable logic-based reasoning framework for emotional support strategy selection, which unifies interpretability with stable performance. Experimental results on public datasets demonstrate that our approach consistently outperforms multiple competitive baselines in both automatic and human evaluations, validating its effectiveness.

The proliferation of Internet of Things (IoT) devices is enabling transformative applications across domains such as smart cities, healthcare, and environmental monitoring. However, the large-scale deployment of these devices also raises sustainability concerns. The integration of intelligence at the edge through the emergence of the Artificial Intelligence of Things (AIoT) amplifies computational requirements and energy consumption. This increase is especially pronounced in resource-constrained settings, consequently exacerbating their ecological impacts. To address these energy and environmental challenges, recent research has increasingly focused on energy harvesting (EH) techniques, energy-efficient sensing methods, and ultra-low-power computing architectures. This survey provides a comprehensive review of the current state of ecological and sustainable AIoT systems powered by energy harvesting. It systematically categorizes existing approaches based on energy sources, hardware-software co-optimization strategies, and communication protocols. In addition, it critically analyzes key challenges, particularly energy intermittency, system scalability, and the balance between performance and energy efficiency. This survey also highlights promising research directions, such as bio-inspired energy harvesting, neuromorphic computing, and integrated sensing-communication frameworks. By summarizing current advances and highlighting open research challenges, this survey aims to provide a structured roadmap for the development of next-generation sustainable AIoT systems.

Persuasion, a vital social skill, influences beliefs, attitudes, and behaviors through conversation. Yet, current dialogue agents either rely on scenario-specific strategies, restricting their cross-context adaptability, or neglect persuasion’s logical structure. They focus on isolated strategy classification, overlooking the significance of fine-grained sequential planning for real-world scenarios. To address these limitations, inspired by basic human mental activities, we present PersuHSG, an adaptive persuasion strategy planning framework. The core idea is to conceptualize persuasion as a tripartite framework comprising cognition, affection, and volition, with each stage represented as a graph layer and principle-based strategies for efficient multi-stage persuasion. Specifically, we first develop PersuInstruct, a fine-tuning dataset to improve dialogue agents’ strategic planning and response generation. Then, we propose a graph-aware planning algorithm for stage-strategy-response reasoning to generate persuasive responses for diverse scenarios. Extensive experiments confirm that PersuHSG significantly enhances the persuasiveness of Large Language Models (LLMs), allows smaller models (e.g., 9B, 13B) to achieve competitive performance, and demonstrates the efficacy of structured strategy planning in improving model efficiency and adaptability.

Abstract Persuasion, as one of the crucial abilities in human communication, has garnered extensive attention from researchers within the field of intelligent dialogue systems. Developing dialogue agents that can persuade others to accept certain standpoints is essential to achieving truly intelligent and anthropomorphic dialogue systems. Benefiting from the substantial progress of Large Language Models (LLMs), dialogue agents have acquired an exceptional capability in context understanding and response generation. However, as a typical and complicated cognitive psychological system, persuasive dialogue agents also require knowledge from the domain of cognitive psychology to attain a level of human-like persuasion. Consequently, the cognitive strategy-enhanced persuasive dialogue agent (defined as CogAgent ), which incorporates cognitive strategies to achieve persuasive targets through conversation, has become a predominant research paradigm. To depict the research trends of CogAgent, in this paper, we first present several fundamental cognitive psychology theories and give the formalized definition of three typical cognitive strategies, including the persuasion strategy, the topic path planning strategy, and the argument structure prediction strategy. Then we propose a new system architecture by incorporating the formalized definition to lay the foundation of CogAgent. Representative works are detailed and investigated according to the combined cognitive strategy, followed by the summary of authoritative benchmarks and evaluation metrics. Finally, we summarize our insights on open issues and future directions of CogAgent for upcoming researchers.

A double-ladder pMOS charge pump circuit is proposed in this paper. It requires two phases (and their complements), CMOS standard process, without triple well. With this configuration an output voltage of 10.88 V can be reached with capacitive load, while every device (transistors and capacitors) sustain a maximum voltage not higher than VDD. Taking parasitic capacitances into account, the proposed structure can reach a 93% voltage boosting efficiency and a 52% power efficiency.