Communications engineering最新文献

Rapid and accurate detection of molecular species with a high degree of selectivity and sensitivity constitutes the ultimate goal of designing sensors for various applications, from studying nutrients in soil-water systems to assessing physiological conditions in human health. With recent progress, two-dimensional (2D) transition metal carbides/nitrides have shown great potential for applications such as energy storage and electromagnetic interference shielding. However, the fundamental electrochemical studies and subsequent applications of MXenes for molecular sensing are still in their infancy. Here, we use 2D sheets of titanium carbide (Ti₃C₂T_x) MXene for electrochemical detection of phosphate, a key molecule for sustainability of life on earth, and major contributor to environmental pollution. Ti₃C₂T_x MXene sensors were demonstrated to be highly selective towards phosphate with a sensing range from 1 µM to 350 µM and a limit of detection (LOD) of 1.31 µM. This work lays the foundation for molecular sensing using Ti₃C₂T_x MXene in complex environments.

As the temperature lift increases, heat pump performance declines due to rising throttling losses in expansion valves. Ejectors present a promising alternative, enabling power recovery from the throttling process. However, fixed-geometry ejectors suffer performance degradation under off-design conditions. Here we propose leveraging the component migration characteristics of zeotropic refrigerants to continuously adjust critical ejector parameters by modulating the quality. This approach enables optimal performance across varying conditions without mechanical modifications. A vapor-injection heat pump cycle incorporating a component-adjustable ejector with three tunable parameters is developed. The concept of equivalent ejector efficiency was introduced, and the adjustment capability of typical high-temperature refrigerant mixtures was investigated, such as mixtures of butane (R600) and synthetic refrigerants (R245fa). Results show that, at a nominal heating capacity of 100 kW, R600/R245fa achieves a lower adjustable limit of -5%, compared to -2% for R1224yd(Z)/R1233zd(E) (synthetic refrigerant mixtures). These findings demonstrate the feasibility of efficient operation of fixed-geometry ejectors under variable conditions. The proposed system and regulation strategy offer a promising design alternative for industrial high-temperature heat pump applications.

The widespread adoption of multirotor uncrewed aerial vehicles (UAVs) is hindered by limited flight time and degraded stability to wind disturbances. Perching, inspired by birds, offers a solution by saving energy and maintaining stability. However, current perching methods often rely on heavy mechanisms, have limitations on surface materials, and obstruct vision sensors. In this study, we propose a process to enable multirotors to perch, land, and detach, all with standard propeller guards. This approach eliminates additional mechanisms, avoids sensor interference, and significantly reduces energy consumption by leveraging the ceiling effect. In over 150 perching and detaching tests with various surface orientations and materials, the approach achieved a 100% success rate and saved 25% - 100% energy consumption compared to hovering. It also improved positioning accuracy by 50 times under strong winds by ensuring stable environmental contact. We demonstrated in real-world environments, showcasing the autonomous perching and detaching ability using only onboard sensing and computation.

The exceptional adaptability of biological organisms to new environments is difficult to replicate in artificial robots. Although dandelion-inspired microfliers have successfully mimicked passive wind dispersal of dandelion seeds with long travel distances and certain controllable landing capacities, existing designs have yet to replicate the adaptive abscission and continuous-shape-changing mechanisms observed in species. Here we propose a programmable adaptive continuous-shape-changing solar-powered microflier enabled by a drone-mounted releasing module and actuated by the shape memory alloy actuators with a minimal total weight of 198 mg. The designed microfliers synergistically mimic the adaptive abscission and continuous-shape-changing mechanisms of dandelion seeds, enabling swarm adjustments of aerodynamics and flight performances in response to multi-environmental factors. Moreover, our microfliers demonstrate controlled interval deployments, programmable flight performances, and robust self-sustaining operations, facilitating their level of intelligence and practical applications.

Engineering design and manufacture are inherently multimodal activities in which engineers consult and produce diverse data and representations across various engineering disciplines and product lifecycle stages. Although well-established digital formats exist for these representations, their use remains restricted within specialist applications, creating silos that limit cross-domain integration. Here we introduce mechanical retrieval-augmented generation (MechRAG), a multimodal large language model architecture designed to unify information from multiple engineering representations typically found in computer-aided engineering and computer-aided design environments. Results demonstrate that MechRAG achieves high accuracy in routinely performed mechanical activities such as data-management or classification tasks, and effectively replicates engineer-level reasoning in more inferential and subjective contexts. Our findings suggest that such conversational interfaces enhance engineering productivity, facilitate more interactive paradigms, and drive transformative workflows across various stages of design and manufacturing.

The exponential growth of large-scale telescope arrays has boosted time-domain astronomy development but introduced operational bottlenecks, including labor-intensive observation planning, data processing, and real-time decision-making. Here we present the StarWhisper Telescope system, an AI agent framework automating end-to-end astronomical observations for surveys like the Nearby Galaxy Supernovae Survey. By integrating large language models with specialized function calls and modular workflows, StarWhisper Telescope autonomously generates site-specific observation lists, executes real-time image analysis via pipelines, and dynamically triggers follow-up proposals upon transient detection. The system reduces human intervention through automated observation planning, telescope controlling and data processing, while enabling seamless collaboration between amateur and professional astronomers. Deployed across Nearby Galaxy Supernovae Survey's network of 10 amateur telescopes, StarWhisper Telescope has detected transients with promising response times relative to existing surveys. Furthermore, StarWhisper Telescope's scalable agent architecture provides a blueprint for future facilities like the Global Open Transient Telescope Array, where AI-driven autonomy will be critical for managing 60 telescopes.

Arrays of coupled nanomagnets have wide-ranging fundamental and practical applications in artificial spin ices, reservoir computing and spintronics. However, lacking in these fields are nanomagnets with perpendicular magnetic anisotropy with sufficient magnetostatic interaction. This would not only open up unexplored possibilities for artificial spin ice geometries but also enable novel coupling methods for applications. Here, we demonstrate a method to engineer the energy landscape of artificial spin lattices with perpendicular magnetic anisotropy. With this, we are able to realize for the first time strongly magnetostatically-coupled 2D lattices of out-of-plane Ising spins that spontaneously order at room temperature on timescales that can be precisely engineered. We show how this property, together with straightforward electrical interfacing, make this system a promising platform for reservoir computing. Our results open the way to investigate the thermodynamics of out-of-plane magnetostatically coupled nanomagnet arrays with novel spin ice geometries, as well as to exploit such nanomagnet arrays in unconventional computing, taking advantage of the adjustable temporal dynamics and strong coupling between nanomagnets.

Rapid and accurate simulations of fluid dynamics around complicated geometric bodies are critical in a variety of engineering and scientific applications. While scientific machine learning (SciML) has shown considerable promise, most studies in this field are limited to simple geometries. This paper addresses this gap by benchmarking diverse SciML models, including neural operators and vision transformer-based foundation models, for fluid flow prediction over intricate geometries. We evaluate the impact of geometric representations-Signed Distance Fields (SDF) and binary masks-on model accuracy, scalability, and generalization using a high-fidelity dataset of steady-state flow over complex geometries. We introduce a unified scoring framework that integrates metrics for global accuracy, boundary layer fidelity, and physical consistency. Our findings reveal that newer foundation models significantly outperform neural operators, particularly in data-limited scenarios. In addition, binary mask representation enhances the performance of vision transformer models by up to 10%, while SDF representations improve neural operator performance by up to 7%. Despite these promises, all models struggle with out-of-distribution generalization, highlighting a critical challenge for future SciML applications. Our work paves the way for robust and scalable ML solutions for fluid dynamics across complex geometries.

Despite the rapid development of autonomous driving, drivers still need to take over when autonomous driving exceeds its design scope or malfunctions. The role of drivers is undergoing a significant transformation from operators to backup users. The existing human driving behaviour models focus more on the behaviour of humans as operators, with static and time-invariant characteristics. However, as backup users, human behaviour characteristics during the takeover process exhibit dynamic and time-varying characteristics, and traditional driver models can no longer describe these, making it difficult to support the safe development of autonomous driving. Unfortunately, the evolution mechanism of driver behaviour is unclear, which has led to the continuous occurrence of accidents in autonomous vehicles. To support the safe development of autonomous driving, we studied the changes in drivers' cognition, decision-making, and control behaviours during the takeover, revealing the evolution mechanism of driver behaviours during the takeover. On this basis, a progressive reshaping model of human driving behaviours is constructed. The comparison with actual driver control data shows that the accuracy of the proposed model is 88.57%, providing a new perspective for understanding driver behaviour during emergency takeover and having certain application value in the research of autonomous driving technology.

Iontronics combines ions as information carriers with electronic-like operations, enabling the creation of ion-based integrated circuits that offer unique signal processing, chemical regulation, and enhanced bio-integrability. Existing simulation tools encounter difficulties in effectively modeling integrated iontronic components, highlighting the need for specialized design and simulation methodologies. Here we present a design approach toward ion-based large-scale integrated circuits, inspired by electronic integrated circuit abstraction levels. We develop a compact model for the iontronic bipolar diode, with a conceptual framework applicable to other iontronic components. The model is implemented using standard Electronic Design Automation tools, allowing simulation of static and dynamic properties of iontronic circuits. Simulated results match measurements from fabricated small-scale iontronic circuits. The proposed simulation approach employs Monte Carlo methodology and enables exploration of how component non-uniformity influences circuit behavior. We demonstrate the model's utility by simulating ion-based integrated circuits, including an iontronic decoder and diode bridge. Expanding traditional circuit design tools to support iontronics could advance the development of hybrid systems that leverage both electronic and ionic functionalities.