Identification of Lighting Strike Damage and Prediction of Residual Strength of Carbon Fiber-Reinforced Polymer Laminates Using a Machine Learning Approach.

Due to the complex and uncertain physics of lightning strike on carbon fiber-reinforced polymer (CFRP) laminates, conventional numerical simulation methods for assessing the residual strength of lightning-damaged CFRP laminates are highly time-consuming and far from pretty. To overcome these challenges, this study proposes a new prediction method for the residual strength of CFRP laminates based on machine learning. A diverse dataset is acquired and augmented from photographs of lightning strike damage areas, C-scan images, mechanical performance data, layup details, and lightning current parameters.

Integrated Sensing and Communication Target Detection Framework and Waveform Design Method Based on Information Theory.

Target detection is a core function of integrated sensing and communication (ISAC) systems. The traditional likelihood ratio test (LRT) target detection algorithm performs inadequately under low signal-to-noise ratio (SNR) conditions, and the performance of mainstream orthogonal frequency division multiplexing (OFDM) waveforms declines sharply in high-speed scenarios. To address these issues, an information-theory-based orthogonal time frequency space (OTFS)-ISAC target detection processing framework is proposed.

A Complete Coverage Path Planning Algorithm for Lawn Mowing Robots Based on Deep Reinforcement Learning.

This paper introduces Re-DQN, a deep reinforcement learning-based algorithm for comprehensive coverage path planning in lawn mowing robots. In the fields of smart homes and agricultural automation, lawn mowing robots are rapidly gaining popularity to reduce the demand for manual labor. The algorithm introduces a new exploration mechanism, combined with an intrinsic reward function based on state novelty and a dynamic input structure, effectively enhancing the robot's adaptability and path optimization capabilities in dynamic environments.

Deep Reinforcemnet Learning for Robust Beamforming in Integrated Sensing, Communication and Power Transmission Systems.

A communication network integrating multiple modes can effectively support the sustainable development of next-generation wireless communications. Integrated sensing, communication, and power transfer (ISCPT) represents an emerging technological paradigm that not only facilitates information transmission but also enables environmental sensing and wireless power transfer. To achieve optimal beamforming in transmission, it is crucial to satisfy multiple constraints, including quality of service (QoS), radar sensing accuracy, and power transfer efficiency, while ensuring fundamental system performance.

Investigating the design characteristics and parameter laws of bird-like flapping-wing aerial vehicles from the perspective of scaling.

Bird-like flapping-wing aerial vehicles (BFAVs) represent a significant advancement in the application of bird biology to aircraft design, with scaling analysis serving as an effective tool for identifying this design process. From the perspective of aviation designers, this paper systematically organizes the scaling laws of birds that are closely related to the design of BFAVs. An intriguing topic further explored is the comparison between birds and BFAVs from the standpoint of scaling, along with an examination of the differences in relevant design parameters.

Robust yet Self-Healing Multimodal Actuators Enabled by Noncovalent Assembled Nanostructure.

In nature, animals can realize multimodal movements such as walking, climbing, and jumping through transformation in locomotor gaits or form for survival, which is highly desired for untethered flexible actuators yet remains challenging. Here, we propose a robust self-healing multimodal actuator enabled by noncovalent assembled nanostructures with elaborate regulation of multistage responsive behaviors. Owing to the dynamic interfacial design between multiple components, the stimulus can be accurately delivered through a "light-heat-force release" pathway, endowing the actuator with diverse motion capabilities and desired jumping ability (27 cm, 34 times body length).

Effect of eHMI-equipped automated vehicles on pedestrian crossing behavior and safety: A focus on blind spot scenarios.

Blind spot collisions are a critical and often overlooked threat to pedestrian safety, frequently resulting in severe injuries. This study investigates the impact of automated vehicles equipped with external human-machine interfaces (eHMIs) on pedestrian crossing behavior and safety, focusing on scenarios where AVs create mutual blind spots between pedestrians and adjacent traffic. A virtual reality experiment with 51 participants simulated crossing situations in front of yielding trucks with obstructed pedestrian visibility, featuring three eHMIs: 'Walk,' 'Don't Walk,' and 'Caution! Blind Spots'.

Integration of ordered porous materials for targeted three-component gas separation.

Separation of multi-component mixtures in an energy-efficient manner has important practical impact in chemical industry but is highly challenging. Especially, targeted simultaneous removal of multiple impurities to purify the desired product in one-step separation process is an extremely difficult task. We introduced a pore integration strategy of modularizing ordered pore structures with specific functions for on-demand assembly to deal with complex multi-component separation systems, which are unattainable by each individual pore.

Constructing Neuron-like Structured NiS/MOF Composites with Enhanced Regulation of Electron Transport and Active Sites for Oxygen Evolution.

Constructing fast electron transfer pathways and abundant electro-active sites is an effective strategy to improve the oxygen evolution reaction (OER) performance of catalysts. Herein, structural engineering and dual-phase engineering were employed to construct a NiS nanoparticle-encapsulated MOF configured with a pseudo-neuronal structure (NiS/MOF/HT). It was found that the pseudo-neuronal structure, constructed with a carbon nanohorn (CNH) and carbon nanotube (CNT), provided fast electron transfer pathways and abundant exposed active sites.

Event-triggered hybrid stubborn ESO for networked systems with uncertain disturbances and stochastic deception attacks.

In this paper, the state estimation problem is investigated for a general class of nonlinear networked systems subject to both external disturbances and stochastic deception attacks. In the presence of deception attacks, a novel hybrid stubborn extended state observer (ESO) is established to estimate the states and total disturbances, simultaneously. In addition, the event-triggered mechanism (ETM) is introduced utilizing the estimation errors to relieve the burden of the transmission networks.

Real-Time Orbit Determination of Micro-Nano Satellite Using Robust Adaptive Filtering.

Low-performing GPS receivers, often used in challenging scenarios such as attitude maneuver and attitude rotation, are frequently encountered for micro-nano satellites. To address these challenges, this paper proposes a modified robust adaptive hierarchical filtering algorithm (named IARKF). This algorithm leverages robust adaptive filtering to dynamically adjust the distribution of innovation vectors and employs a fading memory weighted method to estimate measurement noise in real time, thereby enhancing the filter's adaptability to dynamic environments.

Synth-CLIP: Synthetic data make CLIP generalize better in data-limited scenarios.

Prompt learning is a powerful technique that enables the transfer of Vision-Language Models (VLMs) like CLIP to downstream tasks. However, when the prompt-based methods are fine-tuned solely on base classes, they often struggle to generalize to novel classes lacking visual samples during training, especially in scenarios with limited training data. To address this challenge, we propose an innovative approach called Synth-CLIP that leverages synthetic data to enhance CLIP's generalization capability for base classes and the general capability for novel classes.

Boosting the Actuation Performance of a Dynamic Supramolecular Polyurethane-Urea Elastomer via Kinetic Control.

The ongoing soft actuation has accentuated the demand for dielectric elastomers (DEs) capable of large deformation to replace the traditional rigid mechanical apparatus. However, the low actuation strain of DEs considerably limits their practical applications. This work developed high-performance polyurethane-urea (PUU) elastomers featuring large actuation strains utilizing an approach of kinetic control over the microphase separation structure during the fabrication process.

Neural network-based aeroelastic system identification for predicting flutter of high flexibility wings.

Flutter is an extremely significant academic topic in both aerodynamics and aircraft design. Since flutter can cause multiple types of phenomena including bifurcation, period doubling, and chaos, it becomes one of the most unpredictable instability phenomena. The complexity of modeling aeroelasticity of high flexibility wings will be substantially simplified by investigating the prospect of system identification techniques to forecast flutter velocity.

Robust STAP with coprime sampling structure based on optimal singular value thresholding.

In radar systems, element gain-phase errors can degrade the performance of space-time adaptive processing (STAP), and even cause complete failure. To address this issue, the STAP with the coprime sampling structure based on optimal singular value thresholding is proposed. The algorithm corrects errors by adding four calibrated auxiliary elements and auxiliary pulses to the original array and pulse sequence, while maintaining the coprime sampling structure.

A novel quantitative diagnosis method for rolling bearing faults based on digital twin model.

Dual-impulse behaviors of rolling bearings have been widely researched for quantitative diagnosis. However, it is challenging to accurately extract entry and exit moments of the fault from noise-contaminated raw signals. To address this issue, a novel quantitative diagnosis method based on digital twin model is proposed to assess the fault severity from the original signal waveform.

Neural network-based dynamic target enclosing control for uncertain nonlinear multi-agent systems over signed networks.

Neural networks have significant advantages in the estimation of uncertainty dynamics, which can afford highly accurate prediction outcomes and enhance control robustness. With this in mind, this study presents a neural network-based method to investigate the uncertain target enclosing control problem for multi-agent systems over signed networks. Firstly, a nominal target enclosing controller is constructed by adding the target information component into the classical bipartite consensus error, in which the multi-agent system can be grouped to enclose the target from opposite sides.

Extended target tracking with mobility based on GPR-AUKF.

Simultaneously estimating the kinematic state and extent of extended targets is a nonlinear and high-dimensional problem. While the extended Kalman filter (EKF) is widely employed to achieve this goal, it may not be sufficient for mobility targets. To address this issue, this paper first proposes to embed unscented Kalman filter (UKF) into Gaussian process regression (GPR) since the superiority of UKF to high nonlinear.

One-dimensional mapping of femtosecond laser filaments using coherent microwave scattering.

This paper reports on the use of coherent microwave scattering (CMS) for spatially resolved electron number density measurements of elongated plasma structures induced at mid-IR femtosecond filamentation in air. The presented studies comprise one-dimensional mapping of laser filaments induced via 3.9 µm, 127.

Influence of Left Subclavian Artery Stent Graft Geometry on Blood Hemodynamics in Thoracic Endovascular Aortic Repair.

The objective of this research is to analyze the hemodynamic differences in five configurations of left subclavian artery (LSA) stent grafts after LSA endovascular reconstruction in thoracic endovascular aortic repair (TEVAR). For numerical simulation, one three-dimensional thoracic aortic geometry model with an LSA stent graft retrograde curved orientation was reconstructed from post-TEVAR computed tomography angiography (CTA) images, and four potential LSA graft configurations were modified and reconstructed: three straight (0, 2, and 10 mm aortic extension) and one anterograde configuration. The blood perfusion of the LSA, flow field, and hemodynamic wall parameters were analyzed.

Triboelectric Nanogenerator-Embedded Intelligent Self-Aligning Roller Bearing with the Capability of Self-Sensing, Monitoring, and Fault Diagnosis.

Monitoring the dynamic behaviors of self-aligning roller bearings (SABs) is vital to guarantee the stability of various mechanical systems. This study presents a novel self-powered, intelligent, and self-aligning roller bearing (I-SAB) with which to monitor rotational speeds and bias angles; it also has an application in fault diagnosis. The designed I-SAB is compactly embedded with a novel sweep-type triboelectric nanogenerator (TENG).

A Shortest Distance Priority UAV Path Planning Algorithm for Precision Agriculture.

Unmanned aerial vehicles (UAVs) have made significant advances in autonomous sensing, particularly in the field of precision agriculture. Effective path planning is critical for autonomous navigation in large orchards to ensure that UAVs are able to recognize the optimal route between the start and end points. When UAVs perform tasks such as crop protection, monitoring, and data collection in orchard environments, they must be able to adapt to dynamic conditions.

Dynamic Thermo-Mechanical Properties of Carbon Nanotube Resin Composite Films.

In this paper, we prepared carbon nanotube (CNT) epoxy composite films and conducted tensile experiments at various temperatures (-40 °C, -10 °C, 20 °C, and 50 °C) and frequencies (1 Hz, 10 Hz, and 20 Hz) using Dynamic Mechanical Analysis (DMA). This study reveals the effects of temperature and frequency on the mechanical properties of CNT films and CNT epoxy composite films. The results indicate that the energy storage modulus of the pure CNT film is approximately 13 times greater than that of the composite material at 20 °C.

High-Sensitivity, High-Resolution Miniaturized Spectrometers for Ultraviolet to Near-Infrared Using Guided-Mode Resonance Filters.

Miniaturized spectrometers have significantly advanced real-time analytical capabilities in fields such as environmental monitoring, healthcare diagnostics, and industrial quality control by enabling precise on-site spectral analysis. However, achieving high sensitivity and spectral resolution within compact devices remains a significant challenge, particularly when detecting low-concentration analytes or subtle spectral variations critical for chemical and molecular analysis. This study introduces an innovative approach employing guided-mode resonance filters (GMRFs) to address these limitations.