Photorealistic self-avatars with facial tracking bring VR communication closer to face-to-face interaction, but also expose a fundamental limitation: people often lack accurate awareness and control of their own facial expressions. As a result, directly mirroring facial movements may not reliably convey users’ intended expressions in social VR. We propose modulating rendered expressions on avatars to improve social interaction in VR (e.g., exaggerating positive ones). To design effective modulation strategies and understand how modulation impacts user behavior and perception, we invited 18 participants to perform emotional expressions under varying modulation levels. We collected their self-reported ratings on accuracy and social appropriateness, as well as naturalness ratings and emotion recognition results from 18 additional observers who viewed the expressions. Results show that suppressing rendered expressions amplified participants’ actual facial movements, with effects differing by expression type. Based on our findings, we calculated the modulation ranges for different expressions and developed a real-time modulation system that exaggerates users’ facial expressions with a mild amplitude. We evaluated the system through a VR speech task where 12 participants delivered speeches with and without modulation. Results showed that the applied modulation improved emotional expressiveness and performance. Finally, we developed three application prototypes to illustrate the broader practical implications of our findings.

Virtual Reality (VR) enables users to engage with capabilities beyond human limitations, but it is not always obvious how to trigger these capabilities. Taking the lens of Affordance [35], we believe avatar design is the key to solving this issue, which ideally should communicate its capabilities and how to activate them. To understand the current practice, we selected eight capabilities across four categories and invited twelve professional designers to design avatars that communicate the capabilities and their corresponding interactions. From the resulting designs, we formed 16 guidelines to provide general and category-specific recommendations. Then, we validated these guidelines by letting two groups of twelve participants design avatars with and without guidelines. Participants rated the guidelines’ clarity and usefulness highly. External judges confirmed that avatars designed with the guidelines were more intuitive in conveying the capabilities and interaction methods. Finally, we demonstrated the applicability of the guidelines in avatar design for four VR applications.

The control moment gyroscope (CMG) is widely used for high-precision attitude control in aerospace applications due to its excellent torque output and energy efficiency. However, the micro-vibrations generated during CMG operation can propagate through flexible vibration isolation platforms, which may affect the performance of sensitive spacecraft payloads. To address this critical issue, this article proposes a systematic algorithm that combines coupled dynamic modeling with intelligent control. First, a six-degree-of-freedom (six-DoF) dynamic model accounting for the flexibility of the vibration isolator is developed, which combines the flywheel rotor, gimbal system, and the vibration isolator with a flexible platform to characterize their coupled dynamics. Based on this model, an active-passive isolation control algorithm is designed, which combines feedforward disturbance compensation with a policy iteration algorithm based on adaptive dynamic programming (ADP). The feedforward component enhanced by neural networks effectively approximates and cancels out the interference caused by CMG in real time, while ADP optimization ensures adaptive and optimal vibration suppression. The simulation results demonstrate the performance of the proposed algorithm, confirming a significant reduction in vibration transmission and a marked improvement in isolation efficiency.

Modern engineering, spanning electrical, mechanical, aerospace, civil, and computer disciplines, stands as a cornerstone of human civilization and the foundation of our society. However, engineering design poses a fundamentally different challenge for large language models (LLMs) compared with traditional textbook-style problem solving or factual question answering. Although existing benchmarks have driven progress in areas such as language understanding, code synthesis, and scientific problem solving, real-world engineering design demands the synthesis of domain knowledge, navigation of complex trade-offs, and management of the tedious processes that consume much of practicing engineers' time. Despite these shared challenges across engineering disciplines, no benchmark currently captures the unique demands of engineering design work. In this work, we introduce EngDesign, an Engineering Design benchmark that evaluates LLMs' abilities to perform practical design tasks across nine engineering domains. Unlike existing benchmarks that focus on factual recall or question answering, EngDesign uniquely emphasizes LLMs' ability to synthesize domain knowledge, reason under constraints, and generate functional, objective-oriented engineering designs. Each task in EngDesign represents a real-world engineering design problem, accompanied by a detailed task description specifying design goals, constraints, and performance requirements. EngDesign pioneers a simulation-based evaluation paradigm that moves beyond textbook knowledge to assess genuine engineering design capabilities and shifts evaluation from static answer checking to dynamic, simulation-driven functional verification, marking a crucial step toward realizing the vision of engineering Artificial General Intelligence (AGI).

With the development of IoT technology, the demand for fast and reliable information transmission has become critical. However, in practical application scenarios (vehicle-to-vehicle communication), challenges such as communication channel congestion and interference often arise, which may affect the timeliness of messages. This paper investigates the age of information (AoI) minimization problem in an IRS-assisted wireless powered sensor network (WPSN) under imperfect channel state information (CSI). To support real-time status updates in vehicular environments, we consider a scenario where energy-harvesting sensor nodes, such as those deployed on vehicles, report timely information to a base station with the aid of a reconfigurable intelligent surface (IRS). A closed-form expression of the average AoI is derived based on the energy harvesting and transmission outage probabilities. To ensure robustness against CSI uncertainty, a distributionally robust optimization (DRO) framework based on Conditional Value-at-Risk (CVaR) is proposed. The resulting problem is transformed into a deterministic convex program and solved efficiently via alternating optimization. Simulation results indicate that the proposed algorithm outperforms non-robust algorithms in terms of both timeliness and robustness.