Dictionary trained attention constrained low rank and sparse autoencoder for hyperspectral anomaly detection.

Dictionary representations and deep learning Autoencoder (AE) models have proven effective in hyperspectral anomaly detection. Dictionary representations offer self-explanation but struggle with complex scenarios. Conversely, autoencoders can capture details in complex scenes but lack self-explanation.

Control and Modelling of Laser Shock Peening without Coating (LSPwC) Texture of AISI 9310 Steel.

LSPwC is an important development of Laser shock peening (LSP) technology, and surface texturing is an effective method to improve tribological properties. The combination of these is expected to innovate a new surface texturing technology with a strengthing effect, but no one has attempted it. In this paper, a new LSPTwC technology combining them is innovatively proposed and validated on AISI 9310 steel, which is commonly used in helicopter transmission components for surface texturing.

A Calculation Method of Bearing Balls Rotational Vectors Based on Binocular Vision Three-Dimensional Coordinates Measurement.

The rotational speed vectors of the bearing balls affect their service life and running performance. Observing the actual rotational speed of the ball is a prerequisite for revealing its true motion law and conducting sliding behavior simulation analysis. To address the need for accuracy and real-time measurement of spin angular velocity, which is also under high-frequency and high-speed ball motion conditions, a new measurement method of ball rotation vectors based on a binocular vision system is proposed.

A Three-Dimensional Modeling Approach for Carbon Nanotubes Filled Polymers Utilizing the Modified Nearest Neighbor Algorithm.

Carbon nanotubes (CNTs) are extensively utilized in the fabrication of high-performance composites due to their exceptional mechanical, electrical, and thermal characteristics. To investigate the mechanical properties of CNTs filled polymers accurately and effectively, a 3D modeling approach that incorporates the microstructural attributes of CNTs was introduced. Initially, a representative volume element model was constructed utilizing the modified nearest neighbor algorithm.

Programmable heterogeneous lamellar lattice architecture for dual mechanical protection.

Shear bands frequently appear in lattice architectures subjected to compression, leading to an unstable stress-strain curve and global deformation. This deformation mechanism reduces their energy absorption and loading-bearing capacity and causes the architectures to prioritize mechanical protection of external components at the expense of the entire structure. Here, we leverage the design freedom offered by additive manufacturing and the geometrical relation of dual-phase nanolamellar crystals to fabricate heterogeneous lamellar lattice architectures consisting of body-centered cubic (BCC) and face-centered cubic (FCC) unit cells in alternating lamella.

A cluster-based incremental potential approach for reduced order homogenization of bones.

We develop a cluster-based model order reduction (called C-pRBMOR) approach for efficient homogenization of bones, compatible with a large variety of generalized standard material (GSM) models. To this end, the pRBMOR approach based on a mixed incremental potential formulation is extended to a clustered version for a significantly improved computational efficiency. The microscopic modeling of bones falls into a mixed incremental class of the GSM framework, originating from two potentials.

Multifunctional Intelligent Reconfigurable Metasurface.

The emergent reconfigurable metasurfaces (RMs) have attracted a lot of attention due to their potential in broad applications. As a general platform, RMs are able to control the reflection (or refraction) of incident waves with predefined functionalities. Nevertheless, the operation of RMs is highly dependent on the arrival direction of incidence.

Development of a Highly Sensitive and Stretchable Charge-Transfer Fiber Strain Sensor for Wearable Applications.

Wearable electronics have significantly advanced the development of highly stretchable strain sensors, which are essential for applications such as health monitoring, human-machine interfaces, and energy harvesting. Fiber-based sensors and polymeric materials are promising due to their flexibility and tunable properties, although balancing sensitivity and stretchability remains a challenge. This study introduces a novel composite strain sensor that combines poly(3-hexylthiophene) and tetrafluoro-tetracyanoquinodimethane to form a charge-transfer complex (CTC) with carbon nanotubes (CNTs) on a styrene-butadiene-styrene substrate.

A Novel Cross Tetrachiral Honeycomb Metamaterial with Designable Static and Dynamic Performances.

A novel cross tetrachiral honeycomb metamaterial is proposed, which not only possesses the negative Poisson's ratio property, but also has a wide-frequency bandgap. The effective elastic parameters of the cross tetrachiral honeycomb are first theoretically analyzed; then, its designable performances for negative Poisson's ratio and elastic modulus are studied by varying geometric parameters. The dynamic properties of the cross tetrachiral honeycomb metamaterial are investigated by analyzing the band structure.

Simulation Analysis of Thermoacoustic Effect of CNT Film with Metasurface-Enhanced Acoustic Autofocusing.

This study introduces a novel thermoacoustic (TA) focusing system enhanced by Airy beam-based acoustic metasurfaces, significantly improving acoustic focusing and efficiency. The system integrates a TA emitter, fabricated from carbon nanotube (CNT) films, with a binary acoustic metasurface capable of generating quasi-Airy beams. Through finite element simulations, the system's heat conduction, acoustic focusing, and self-healing properties were thoroughly analyzed.

Accurate prediction of discontinuous crack paths in random porous media via a generative deep learning model.

Pore structures provide extra freedoms for the design of porous media, leading to desirable properties, such as high catalytic rate, energy storage efficiency, and specific strength. This unfortunately makes the porous media susceptible to failure. Deep understanding of the failure mechanism in microstructures is a key to customizing high-performance crack-resistant porous media.

Antenna optimization using machine learning with reduced-dimensionality surrogates.

In modern times, antenna design has become more demanding than ever. The escalating requirements for performance and functionality drive the development of intricately structured antennas, where parameters must be meticulously adjusted to achieve peak performance. Often, global adjustments to geometry are necessary for optimal results.

Heat and mass transfer of water flow in graphene nanochannels: effect of pressure and interfacial interaction.

The low-resistance transport of water within graphene nanochannels makes it promising for electronic cooling applications. However, how the water pressure and the water-graphene interaction strength affect the flow field and the thermal transfer, including velocity slip, friction coefficient, Nusselt number, temperature slip, interfacial thermal resistance, and variation of physical properties, is still not clearly understood. In this paper, we employ molecular dynamics (MD) simulations to investigate qualitatively the heat transfer of water flow in graphene nanochannels.

Prediction of Mechanical Properties of Lattice Structures: An Application of Artificial Neural Networks Algorithms.

The yield strength and Young's modulus of lattice structures are essential mechanical parameters that influence the utilization of materials in the aerospace and medical fields. Currently, accurately determining the Young's modulus and yield strength of lattice structures often requires conduction of a large number of experiments for prediction and validation purposes. To save time and effort to accurately predict the material yield strength and Young's modulus, based on the existing experimental data, finite element analysis is employed to expand the dataset.

Counterion Blockade in a Heterogeneously Charged Single-File Water Channel.

The Possion-Nernst-Planck theories fail to describe the ionic transport in Angstrom channels, where conduction deviates from Ohm's law, which is attributed to the dehydration/self-energy barrier and dissociation of Bjerrum ion pairs in previous work. Here, we find that the cations can be strongly bound to the surface charge, which blocks the ionic transport in a single-file water channel, causing nonlinear current-voltage curves. The presence of free ions significantly increases the probability of bound ions being released, resulting in an ionic current.

Crystallographic plane-induced selective mineralization of nanohydroxyapatite on fibrous-grained titanium promotes osteointegration and biocorrosion resistance.

The (002) crystallographic plane-oriented hydroxyapatite (HA) and anatase TiO enable favorable hydrophilicity, osteogenesis, and biocorrosion resistance. Thus, the crystallographic plane control in HA coating and crystalline phase control in TiO is vital to affect the surface and interface bioactivity and biocorrosion resistance of titanium (Ti) implants. However, a corresponding facile and efficient fabrication method is absent to realize the HA(002) mineralization and anatase TiO formation on Ti.

Thermal-Mechanical Coupling Performance of Heat-Resistant, High-Strength and Printable Al-Si Alloy Antisymmetric Lattice Structure.

The unsatisfactory mechanical performance at high temperatures limits the broad application of 3D-printed aluminum alloy structures in extreme environments. This study investigates the mechanical behavior of 4 different lattice cell structures in high-temperature environments using AlSi12Fe2.5Ni3Mn4, a newly developed, heat-resistant, high-strength, and printable alloy.

Constructing the Snail Shell-Like Framework in Thermal Interface Materials for Enhanced Through-Plane Thermal Conductivity.

Melioration of the through-plane thermal conductivity (TC) of thermal interface materials (TIMs) is a sore need for efficient heat dissipation to handle an overheating concern of high-power-density electronics. Herein, we constructed a snail shell-like thermal conductive framework to facilitate vertical heat conduction in TIMs. With inspiration from spirally growing calcium carbonate platelets of snail shells, a facile double-microrod-assisted curliness method was developed to spirally coil boron nitride nanosheet (BNNS)/aramid nanofiber (ANF) laminates where interconnected BNNSs lie along the horizontal plane.

Bearing Dynamics Modeling Based on the Virtual State-Space and Hammerstein-Wiener Model.

This study investigates a novel approach for assessing the health status of rotating machinery transmission systems by analyzing the dynamic degradation of bearings. The proposed method generates multi-dimensional data by creating virtual states and constructs a multi-dimensional model using virtual state-space in conjunction with mechanism model analysis. Innovatively, the Hammerstein-Wiener (HW) modeling technique from control theory is applied to identify these dynamic multi-dimensional models.