Hydrogen offers a credible route to zero-carbon combustion in internal combustion engines and can deliver high thermal efficiency, but its physical and combustion properties create persistent challenges in fuel delivery, storage, and NOx control. This work reports experiments on a boosted single-cylinder spark-ignition engine running on hydrogen direct injection (DI) as the primary fuel, with intake-port water injection used as a NOx mitigation strategy and further enriched with hydrogen-charged nanobubbles. The objectives are to quantify how far water injection can suppress NOx and to test whether the additional hydrogen carried by nanobubbles can recover, or even improve, the power and efficiency lost through water dilution. Bulk nanobubbles (50–500 nm) remain suspended in water for weeks because their negligible buoyancy and strongly negative zeta potential suppress rise and coalescence; despite established uses in agriculture, water treatment, and biomedicine, their application as a hydrogen carrier in a working engine has received very limited experimental attention. A bespoke generator was used to charge water with hydrogen nanobubbles, and their size, concentration, and zeta potential were measured by dynamic light scattering (Malvern Zetasizer Ultra); by optimising the generator, a maximum concentration of 5.12 × 1011 nanobubbles per millilitre was achieved. Hydrogen nanobubble water injection was then compared directly with conventional water injection. Relative to conventional water, nanobubble water raised indicated thermal efficiency by about 4% and torque by about 5%. NOx fell substantially in both cases, with a slightly greater reduction for nanobubble water; this difference is attributed principally to the lower measured lambda of that campaign rather than to a distinct NOx-suppression mechanism. Three independent diagnostics — residual exhaust oxygen, wideband lambda and exhaust-gas temperature — shift consistently in a direction that is compatible with additional in-cylinder hydrogen release from the nanobubble suspension. It must be emphasised, however, that such a release was not measured directly. The present dataset establishes the performance and emissions outcome; the underlying mechanism remains a hypothesis that requires dedicated optical and pressure-decay diagnostics before it can be regarded as demonstrated.
Coupled with vision-based inspection techniques, unmanned aerial vehicles (UAVs) have been extensively applied to power transmission line inspection. UAV images typically cover a wide field of view, they often contain complex backgrounds in power transmission line inspection, which make the accurate detection and localization of small-size defects challenging, especially on resource-constrained devices. To tackle this issue, this paper proposes YOLO-TDH, a novel lightweight object detection framework derived from YOLOv8. The proposed YOLO-TDH incorporates three key components to improve the detection of small-size defects: (1) an Enhanced Feature Integration Module (EFIM), which strengthens multi-scale feature extraction and captures fine-grained details required to distinguish defective components from normal ones; (2) an Enhanced Feature Fusion Module (EFFM), which optimizes feature information flow and reduce the loss of critical details for small-size defects; and (3) a novel Transformer-based decoder, which models global context and alleviates ambiguity among overlapping components. The ShapeIoU loss is employed to improve the bounding box regression accuracy. Experiments on a dedicated transmission line dataset show that YOLO-TDH, Insulator dataset for targeted fault verification, and the public VisDrone2019 benchmark to validate generalization capability. The results demonstrate that YOLO-TDH consistently outperforms existing state-of-the-art lightweight methods in terms of Precision, Recall, mAP@0.5, and mAP@0.5:0.95. Results show that the proposed YOLO-TDH achieves a proper balance between diagnostic accuracy and computational efficiency, providing a robust solution for real-time, fine-grained health monitoring in resource-constrained scenarios.
The combustion behavior of fuel droplets plays a crucial role in controlling emissions and combustion efficiency in internal combustion engines. Droplet heating, evaporation, and burning characteristics strongly influence ignition delay, combustion duration, and soot formation. Oxymethylene ethers (OMEs), particularly oxymethylene ether-3 (OME3), have attracted interest as drop-in diesel alternatives due to their high oxygen content and clean combustion potential. This study experimentally investigates the combustion of a single fuel droplet suspended on a thermocouple and exposed to a controlled environment at 500 degrees C and atmospheric pressure. Four fuels were examined: neat diesel and diesel blends containing 20, 40, and 60 vol% OME3. High-speed imaging was used to analyze ignition delay, combustion duration, and flame luminosity, complemented by scanning electron microscopy (SEM) of collected particulates. Increasing OME3 content significantly reduced ignition delay (by similar to 65-70% for D40-O60) while increasing combustion duration by similar to 25-30%. Flame luminosity decreased markedly, indicating suppression of macroscopic soot. This behavior is primarily attributed to the high oxygen content of OME3, which promotes oxidation pathways that limit soot precursors formation. SEM analysis revealed a shift toward finer particles, with sub-micrometer particle counts increasing from similar to 120 mm(-2) to similar to 340 mm(-2). These results demonstrate that reduced flame luminosity does not necessarily correspond to lower particulate emissions but instead reflects a shift toward ultrafine particle formation. Overall, OME3 modifies combustion behavior by reducing visible soot while increasing ultrafine particle number density, highlighting a critical trade-off in oxygenated fuel performance.
With the advent of 6G communications, intelligent communication systems face multiple challenges, including constrained perception and response capabilities, limited scalability, and low adaptability in dynamic environments. To address these challenges, this tutorial provides a systematic and comprehensive introduction to the principles, design, and applications of Large Artificial Intelligence Models (LAMs) and Agentic AI technologies in intelligent communication systems, aiming to offer researchers an integrated overview of cutting-edge methodologies and practical insights. First, the tutorial outlines the background of 6G communications and reviews the technological evolution from LAMs to Agentic AI. It then systematically examines the key components required for constructing LAMs, classifies various types of LAMs, and analyzes their applicability in communication. A LAM-centric design paradigm tailored for communication systems is subsequently proposed, encompassing dataset construction, internal learning, and external learning approaches. Building upon this foundation, the tutorial develops an LAM-based Agentic AI system for intelligent communications, elaborating on its core components-including agents, world models, planners, knowledge bases, tools, and memory modules-as well as their interaction mechanisms. Finally, it provides an in-depth review of representative applications of LAMs and Agentic AI in communication scenarios, and summarizes the current research challenges and future directions, with the goal of fostering the development of efficient, secure, and sustainable next-generation intelligent communication systems.
Famous face recognition tasks have traditionally been used to diagnose prosopagnosia, offering striking examples of the inability to recognise highly familiar faces. Yet, their popularity has dwindled with the development of standardised unfamiliar face recognition tasks that are less cumbersome to administer and can readily be implemented online. Here, we argue that there is a danger of omitting measures of familiar face recognition from prosopagnosia screening: not only may this challenge the very definition of the condition, but, with some adjustments, famous face recognition tasks can continue to offer highly sensitive measures of everyday face recognition ability. Thus, we developed and evaluated an online, automated famous face recognition paradigm that can readily be implemented into large-scale screening programmes. This task improves on previous designs by (a) eliminating extrinsic cues to identity by including distractor as well as familiar faces, (b) supporting the use of unseen rather than “iconic” images of celebrities, and (c) offering a method for automated scoring. Multiple versions of the task were found to have high sensitivity in the detection of developmental prosopagnosia. When required, sub-scores collected from the same paradigm can be used to assess performance at different stages of recognition and identification, helping to probe more precise loci of impairment. The latter is important to guide the diagnosis of more complex cases and, potentially, their remediation.
