Online detection and evaluation of early fatigue cracks using sideband peak intensity technique with frequency-mismatched excitation pulse-echo method.

This work presents a nonlinear ultrasonic (NLU) technique called sideband peak intensity (SPI) combining an improved pulse-echo (PE) experimental method for online detection and evaluation of fatigue cracks at their early stages. Advantages of the proposed technique are that it enjoys the high sensitivity and ease of application of NLU SPI technique and easy implementation of the PE experimental method. The PE experimental method is improved by adopting frequency-mismatched excitations to enhance the sensitivity and robustness of the SPI technique.

Interface Engineering of Thin-Film Lithium Phosphorus Oxynitride Electrolyte by Appropriate Oxygen Plasma Treatment for Flexible All-Solid-State Supercapacitor.

The interfacial incompatibility between lithium phosphorus oxynitride (LiPON) and anode greatly deteriorates the performance of thin-film all-solid-state supercapacitors (ASSSCs). This article investigates oxygen plasma treatment to improve the interface. Through appropriate plasma treatment, a LiO/LiPO composite layer is formed by replacing nitrogen with oxygen at the LiPON surface owing to strong reactivity of oxygen plasma.

Probe-type optical fiber sensors for electric field distribution measurement.

This paper reports a compact fiber optical electric field (E-field) sensor aiming for the precise detection of transient E-field distributions. Here, a reflective polarization-reciprocal optical path is proposed, which inherently mitigates the temperature-induced birefringence interference of the electro-optical crystal without the need for additional optical elements, thereby facilitating a reduced-size sensing probe. Furthermore, an adaptive particle swarm optimization (A-PSO) algorithm has been utilized for the first time to optimize the insulation structure of the optical E-field sensor, which significantly suppresses field distortion within the sensing region by 50%.

Multi-Intelligent Reflecting Surfaces and Artificial Noise-Assisted Cell-Free Massive MIMO Against Simultaneous Jamming and Eavesdropping.

In a cell-free massive multiple-input multiple-output (MIMO) system without cells, it is assumed that there are smart jammers and disrupters (SJDs) that attempt to interfere with and eavesdrop on the downlink communications of legitimate users. A secure transmission scheme based on multiple intelligent reflecting surfaces (IRSs) and artificial noise (AN) is proposed. First, an access point (AP) selection strategy based on user location information is designed, which aims to determine the set of APs serving users.

Research on SAR image quality evaluation method based on improved harris hawk optimization algorithm and XGBoost.

Synthetic aperture radar (SAR) is crucial for military reconnaissance and remote sensing, but image quality can be affected by various factors, impacting target detection performance. Thus, pre-evaluation of SAR image quality is essential to filter out poor-quality images, optimize resource allocation, and enhance detection accuracy and efficiency. This paper proposes a comprehensive SAR image quality evaluation method combining objective and subjective approaches.

Predicting the compressive strength of high-performance concrete using an interpretable machine learning model.

Reliable predictions of concrete strength can reduce construction time and labor costs, providing strong support for building construction quality inspection. To enhance the accuracy of concrete strength prediction, this paper introduces an interpretable framework for machine learning (ML) models to predict the compressive strength of high-performance concrete (HPC). By leveraging information from a concrete dataset, an additional six features were added as inputs in the training process of the random forest (RF), AdaBoost, XGBoost and LightGBM models, and the optimal hyperparameters of the models were determined using 5-fold cross-validation and random search methods.

Materialism in Chinese college students during 2007-2020: The influence of social change on the inclining trend.

Materialism is fundamental to the human value or goal system; therefore, an understanding of its level among Chinese college students and its changes over time is of great value. In the present study, a cross-temporal meta-analysis was performed by reviewing studies that conducted Material Values Scale-based assessment of the materialism level among Chinese university students from 2007 to 2020. Moreover, a time lag analysis was performed to clarify whether variations in materialism level are interpretable with macro-social indicators.

A Hybrid Perovskite-Based Electromagnetic Wave Absorber with Enhanced Conduction Loss and Interfacial Polarization through Carbon Sphere Embedding.

Electronic equipment brings great convenience to daily life but also causes a lot of electromagnetic radiation pollution. Therefore, there is an urgent demand for electromagnetic wave-absorbing materials with a low thickness, wide bandwidth, and strong absorption. This work obtained a high-performance electromagnetic wave absorption system by adding conductive carbon spheres (CSs) to the CHNHPbI (MAPbI) absorber.

Urban density and spatial carbon emission performance of megacities in China.

Increasing urban density promotes a greater social role for cities. The current low-carbon city development goals put forward higher requirements for carbon performance. Based on the viewpoint of minimizing natural resource input and maximizing value acquisition in ecological benefits, the influence of urban density on spatial carbon performance is analyzed by polynomial function relationship, and Beijing and Shanghai with different urban function positioning are taken as examples for comparative analysis.

Identification of dynamic networks community by fusing deep learning and evolutionary clustering.

Community detection is a critical component of network analysis and a hot topic in social computing. Detecting community structure in dynamic networks has important theoretical and practical implications for understanding the intrinsic function of networks and predicting network behavior. However, the majority of existing dynamic community detection methods adopt shallow models, which have limited ability to excavate complex non-linear structures and tend to generate undesirable community structures.

Trusted artificial intelligence for environmental assessments: An explainable high-precision model with multi-source big data.

Environmental assessments are critical for ensuring the sustainable development of human civilization. The integration of artificial intelligence (AI) in these assessments has shown great promise, yet the "black box" nature of AI models often undermines trust due to the lack of transparency in their decision-making processes, even when these models demonstrate high accuracy. To address this challenge, we evaluated the performance of a transformer model against other AI approaches, utilizing extensive multivariate and spatiotemporal environmental datasets encompassing both natural and anthropogenic indicators.

Variation pattern, influential factors, and prediction models of PM2.5 concentrations in typical urban functional zones of northeast China.

This study investigated the spatial and temporal variations of PM2.5 concentrations in Harbin, China, under the influence of meteorological parameters and gaseous pollutants. The complex relationship between meteorological parameters and pollutants was explored using Pearson correlation analysis and interaction effect analysis.

TVGCN: Time-varying graph convolutional networks for multivariate and multifeature spatiotemporal series prediction.

Spatiotemporal (ST) graph modeling has garnered increasing attention recently. Most existing methods rely on a predefined graph structure or construct a single learnable graph throughout training. However, it is challenging to use a predefined graph structure to capture dynamic ST changes effectively due to evolving node relationships over time.

Enhanced Safety in Autonomous Driving: Integrating a Latent State Diffusion Model for End-to-End Navigation.

Ensuring safety in autonomous driving is crucial for effective motion planning and navigation. However, most end-to-end planning methodologies lack sufficient safety measures. This study tackles this issue by formulating the control optimization problem in autonomous driving as Constrained Markov Decision Processes (CMDPs).

Performance investigation of a solar-driven cascaded phase change heat storage cross-seasonal heating system for plateau applications.

The mismatch between solar radiation resources and building heating demand on a seasonal scale makes cross-seasonal heat storage a crucial technology, especially for plateau areas. Utilizing phase change materials with high energy density and stable heat output effectively improves energy storage efficiency. This study integrates cascaded phase change with a cross-seasonal heat storage system aimed at achieving low-carbon heating.

Research on the Mechanical and Thermal Properties of Carbon-Fiber-Reinforced Rubber Based on a Finite Element Simulation.

The comprehensive performance of rubber products could be significantly improved by the addition of functional fillers. To improve research efficiency and decrease the experimental cost, the mechanical and thermal properties of carbon-fiber-reinforced rubber were investigated using finite element simulations and theoretical modeling. The simplified micromechanical model was constructed through the repeatable unit cell with periodic boundary conditions, and the corresponding theoretical models were built based on the rule of mixture (ROM), which can be treated as the mutual verification.

Improved fixed-time stability analysis and applications to synchronization of discontinuous complex-valued fuzzy cellular neural networks.

This article mainly centers on proposing new fixed-time (FXT) stability lemmas of discontinuous systems, in which novel optimization approaches are utilized and more relaxed conditions are required. The conventional discussions about Vt>1 and 0 View Article and Find Full Text PDF

HeMoDU: High-Efficiency Multi-Object Detection Algorithm for Unmanned Aerial Vehicles on Urban Roads.

Unmanned aerial vehicle (UAV)-based object detection methods are widely used in traffic detection due to their high flexibility and extensive coverage. In recent years, with the increasing complexity of the urban road environment, UAV object detection algorithms based on deep learning have gradually become a research hotspot. However, how to further improve algorithmic efficiency in response to the numerous and rapidly changing road elements, and thus achieve high-speed and accurate road object detection, remains a challenging issue.

Vests with Radiative Cooling Materials to Improve Thermal Comfort of Outdoor Workers: An Experimental Study.

This study focuses on improving human thermal comfort in a high-temperature outdoor environment using vests with a radiative cooling coating. The effects of coating thickness on the radiative cooling performance were first evaluated, and an optimal thickness of 160 μm was achieved. Then, six subjects were recruited to evaluate the thermal comfort in two scenarios: wearing the vest with radiative cooling coatings, and wearing the standard vest.

Optically compatible infrared camouflage performance of ITO ink.

With the rapid development of military reconnaissance technology, reconnaissance devices have been equipped with wideband reconnaissance ability, which imposes increased requirements on camouflage. Developing multiband camouflage materials with good compatibility has become increasingly important. Indium tin oxide (ITO), a transparent conductive oxide with good comprehensive photoelectric properties, exhibits different absorption, reflectivity, and transmission characteristics in different bands of electromagnetic waves.

Mechanism of Rapid Curing Pile Formation on Shoal Foundation and Its Bearing Characteristic.

This study explores the application effect of the new non-isocyanate polyurethane curing agent on the rapid curing mechanism and bearing characteristics of piles in beach foundations. Through laboratory tests and field tests, the effects of the curing agent on the physical and mechanical properties of sand were systematically analyzed, including compressive strength, shear strength, and elastic modulus, and the effects of water content and cement-sand mass ratio on the properties of sand after curing were investigated. The results show that introducing a curing agent significantly improves the mechanical properties of sand, and the cohesion and internal friction angle increase exponentially with the sand mass ratio.