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This article reviews the anti-penetration principles and strengthening mechanisms of metal materials, ranging from macroscopic failure modes to microscopic structural characteristics, and further summarizes the micro–macro correlation in the anti-penetration process. Finally, it outlines the constitutive models and numerical simulation studies utilized in the field of impact and penetration. From the macro perspective, nine frequent penetration failure modes of metal materials are summarized, with a focus on the analysis of the cratering, compression shear, penetration, and plugging stages of the penetration process. The reasons for the formation of adiabatic shear bands (ASBs) in metal materials with different crystal structures are elaborated, and the formation mechanism of the equiaxed grains in the ASB is explored. Both the strength and the toughness of metal materials are related to the materials’ crystal structures and microstructures. The toughness is mainly influenced by the deformation mechanism, while the strength is explained by the strengthening mechanism. Therefore, the mechanical properties of metal materials depend on their microstructures, which are subject to the manufacturing process and material composition. Regarding numerical simulation, the advantages and disadvantages of different constitutive models and simulation methods are summarized based on the application characteristics of metal materials in high-speed penetration practice. In summary, this article provides a systematic overview of the macroscopic and microscopic characteristics of metal materials, along with their mechanisms and correlation during the anti-penetration and impact-resistance processes, thereby making an important contribution to the scientific understanding of anti-penetration performance and its optimization in metal materials.

Abstract To improve the thermal and combustion properties of nanothermites, a design theory of changing the state of matter and structural state of the reactants during reaction was proposed. The Al/MoO3/KClO4 (Kp) nanothermite was prepared and the Al/MoO3 nanothermite was used as a control. SEM and XRD were used to characterize the nanothermites; DSC was used to test thermal properties; and constant volume and open combustion tests were performed to examine their combustion performance. Phase and morphology characterization of the combustion products were performed to reveal the mechanism of the aluminothermic reaction. The results show that the Al/MoO3/Kp nanothermite exhibited excellent thermal properties, with a total heat release of 1976 J·g− 1, increasing by approximately 33% of 1486 J·g− 1 of the Al/MoO3 nanothermite, and activation energy of 269.66 kJ·mol− 1, which demonstrated higher stability than the Al/MoO3 nanothermite (205.64 kJ·mol− 1). During the combustion test, the peak pressure of the Al/MoO3/Kp nanothermite was 0.751 MPa, and the average pressure rise rate was 25.03 MPa·s− 1, much higher than 0.188 MPa and 6.27 MPa·s− 1 of the Al/MoO3 nanothermite. The combustion products of Al/MoO3 nanothermite were Al2O3, MoO, and Mo, indicating insufficient combustion and incomplete reaction, whereas, the combustion products of Al/MoO3/Kp nanothermite were Al2O3, MoO, and KCl, indicating complete reaction. Their “coral-like” morphology was the effect of reactants solidifying after melting during the combustion process. The characterization of reactants and pressure test during combustion reveals the three stages of aluminothermic reaction in thermites. The excellent thermal and combustion performance of Al/MoO3/Kp nanothermite is attributed to the melt and decomposition of Kp into O2 in the third stage. This study provides new ideas and guidance for the design of high-performance nanothermites.

As a major element of the transportation network, tunnels are unavoidably threatened by accidental loads such as vehicle bombs and tank truck explosions. The goal of this research is to explore the dynamic characteristics and damage assessment of tunnel structures under contact blast loads. First, three scaled-down reinforced concrete tunnel models were made, and the explosion test and static loading test were carried out successively to evaluate the axial residual bearing capacity, axial displacement and failure mechanism of the tunnel. Secondly, the finite element model is built by utilizing LS-DYNA, and the reliability of the finite element method is confirmed by comparing the data of the explosion test with the static loading test. At the same time, the calculation method for damage coefficient and the classification criteria for damage grade based on axial residual bearing capacity are presented. Then, based on the finite element method, the propagation process of the explosion shock wave in the tunnel and the damage mechanism of the tunnel are investigated. Finally, seven explosion scenarios are developed, the damage degree of these seven tunnels under the blast load is quantitatively analyzed, and further anti-blast design ideas are put forth. The study in this article may give an intended reference for the damage assessment, anti-explosion design and strengthening work of reinforced concrete tunnels.

Integrating a low-spatial-resolution hyperspectral image with a high-spatial-resolution multispectral image (HR-MSI) is recognized as a valid method for acquiring HR-HSI. Among the current fusion approaches, the tensor ring (TR) decomposition-based method has received growing attention owing to its superior performance in preserving the spatial-spectral correlation. Based on the TR decomposition, the degradation model is developed via the spectral and spatial cores in TR. Here, we study the low-rankness of TR factors from the TNN perspective and consider the mode-2 logarithmic TNN (LTNN) on each TR factor. A novel fusion model is proposed by incorporating this LTNN regularization and the weighted total variation which is to promote the continuity of HR-HSI in the spatial-spectral domain. Meanwhile, we have devised a proximal alternating minimization algorithm to solve the proposed model. The experimental results indicate that our method improves the visual quality and exceeds the existing state-of-the-art fusion approaches concerning various quantitative metrics.

Abstract To improve the thermal properties of thermite safely and stably, electrostatic spraying was used to prepare the Al/MoO3 thermite. The Al/MoO3 thermites were detected and characterized by scanning electron microscopy (SEM) and X‐ray diffraction (XRD), and thermal decomposition experiments were carried out by differential scanning calorimetry (DSC). The heat release of the Al/MoO3 thermite prepared by the electrostatic spray (1044 J g−1) is significantly higher than that of the thermite prepared by the ultrasonic (692 J g−1), which is due to more uniform dispersion between Al and MoO3. The initial reaction temperature and activation energy (Ea) of the former keep it steady. Electrostatic spray ensures the safety and stability of the Al/MoO3 thermite. This study provides a new idea for safely and stably improving the thermal properties of thermite by enhancing surface homogenization, which is of great significance for practical applications.

With a large field of view, remote sensing is a useful technique that can provide a new way to acquire crop acreage and spatial distribution. As an important oil crop, rapeseed is widely planted in many countries. Besides, rapeseed mapping is of great significance for food security and policy regulation. The objective of this work is to explore an automatic and effective framework for rapeseed mapping using multitemporal multispectral images (MSIs) captured by multiple sensors. The automatic rapeseed mapping framework consists of two stages. Concretely, color transfer is introduced and combined with vegetation indexes to detect rapeseed for the first time. After that, the most general supervised classification methods are applied to optimize the initial mapping result. In the experiments, the performance of different methods is evaluated on multisensor data within four years (2017–2020). Moreover, to analyze the effect of temporal information, the mapping performance of using different MSIs as input is systematically compared. Experimental results illustrate the effectiveness of the proposed framework, in which color transfer makes an important role. Some valuable findings are also obtained which can be further used for global rapeseed mapping.

Object search is a challenging yet important task. Many efforts have been made to address this issue and achieve great progress in natural image, yet searching all the specified types of objects from remote sensing image is barely studied. In this article, we are interested in searching objects from remote sensing images. Compared to person search in natural scenes, this task is challenging in two factors: One is that remote image usually contains a large number of objects, which poses a great challenge to characterize the object features; another is that the objects in remote sensing images are dense, which easily yield erroneous localization. To address these issues, we propose a new end-to-end deep learning framework for object search in remote sensing images. First, we propose a multiscale feature aggregation module, which strengthens the representation of low-level features by fusing multilayer features. The fused features with richer details significantly improve the accuracy of object search. Second, we propose a dual-attention object enhancement module to enhance features from channel and spatial dimensions. The enhanced features significantly improve the localization accuracy for dense objects. Finally, we built two challenging datasets based on the remote sensing images, which contain complex changes in space and time. The experiments and comparisons demonstrate the state-of-the-art performance of our method on the challenging datasets.

In this study, A time-domain seismic response analysis method and a calculation model of the underground structure that can realize the input of seismic P, SV and Rayleigh waves are established, based on the viscoelastic artificial boundary elements and the boundary substructure method for seismic wave input. After verifying the calculation accuracy, a comparative study on seismic response of a shallow-buried, double-deck, double-span subway station structure under incident P, SV and Rayleigh waves is conducted. The research results show that there are certain differences in the cross-sectional internal force distribution characteristics of underground structures under different types of seismic waves. The research results show that there are certain differences in the internal force distribution characteristics of underground structures under different types of seismic waves. At the bottom of the side wall, the top and bottom of the center pillar of the underground structure, the section bending moments of the underground structure under the incidences of SV wave and Rayleigh wave are relatively close, and are significantly larger than the calculation result under the incidence of P wave. At the center of the side wall and the top floor of the structure, the peak value of the cross-sectional internal force under the incident Rayleigh wave is larger than the calculation result under SV wave. In addition, the floor of the underground structure under Rayleigh waves vibrates in both the horizontal and vertical directions, and the magnification effect in the vertical direction is more significant. Considering that the current seismic research of underground structures mainly considers the effect of body waves such as the shear waves, sufficient attention should be paid to the incidence of Rayleigh waves in the future seismic design of shallow underground structures.

Heavy rains usually hit Laos countrywide and cause serious floods, influencing local agriculture, households, and the economy. Therefore, it is crucial to monitor the flooding in Laos to better understand the flood patterns and characteristics. This paper aims to analyze the influence of the flooding in Laos with multi-source data, e.g., Synthetic Aperture Radar (SAR), optical multi-spectral images, and geographic information system data. First, the flood areas in Laos from 2018 to 2022 are detected using a decision fusion method. Based on the flood areas and the global Land Use/Land Cover (LULC) product, the macro scale global impact of the flood is analyzed. Second, taking the Vientiane Capital as a case study area, a flood forecasting method is applied to estimate the risk of flooding. Finally, optical images before and after the flood event are extracted for a close-up comparison at the micro scale. Based on the above multi-scale analysis, floods in Laos are found to be predominantly concentrated in the flat areas near the Mekong River, with a decreasing trend over time, which could be helpful for flood management and mitigation strategies in Laos. The validation results exhibited notable average indices across a five-year period, with mIoU (0.7782), F1 score (0.7255), and overall accuracy (0.9854), respectively.

Hyperspectral unmixing (HU) is an efficient way to extract component information from mixed pixels in remotely sensed imagery. Nonnegative matrix factorization (NMF) based unmixing methods have been widely used due to their ability to extract endmembers (pure spectral signatures) and their corresponding fractional abundances in simultaneous fashion. In this article, we propose a new sparse-constrained NMF method for HU purposes. Unlike most sparse regularizers, imposed on abundances (vectors or matrix) directly, our method imposes sparse constraints on a transformed abundance domain. It is based on the assumption that sparsity, when applied on a well-designed transform domain, leads to sparser representations than those in the corresponding source domain (e.g., natural images are approximately sparse in a wavelet domain). In this regard, we specifically explore sparsity on a curvelet transformed domain of abundances. Moreover, we consider the Chambolle-Pock algorithm to solve the involved optimization model, so as to obtain a fast and stable solution. Our experiments, carried out on both synthetic and real hyperspectral datasets, reveal that our newly proposed transform domain-based sparse NMF unmixing method (hereinafter named as “TS-NMF”) obtains better unmixing results than those achieved by other state-of-the-art NMF-based unmixing methods, with less parameter tuning and better robustness to noise.

Tunnels play an essential role in the transportation network. Tunnel entrances are usually buried at a shallow depth. In the event of an internal explosion, the blast pressure will cause severe damage or even collapse of the tunnel entrance, paralyzing the traffic system. Therefore, an accurate assessment of the damage level of tunnel entrances under internal blast loading can provide effective assistance for the anti-blast design of tunnels, post-disaster emergency response, and economic damage assessment. In this paper, four tunnel entrance specimens were designed and fabricated with a scale ratio of 1/5.5, and a series of field blast tests were carried out to examine the damage pattern of the tunnel entrances under internal explosion. Subsequently, static loading tests were conducted to obtain the maximum bearing capacity of the intact specimen and residual bearing capacities of the post-blast specimens. After that, an explicit non-linear analysis was carried out and a numerical finite element (FE) model of the tunnel entrance under internal blast loading was established by adopting the arbitrary Lagrangian–Eulerian (ALE) method and validated based on the data obtained from the field blast and static loading tests. A probabilistic vulnerability analysis of a typical tunnel entrance subjected to stochastic internal explosions (assuming various charge weights and detonation points) was then carried out with the validated FE model. For the purpose of damage assessment, the residual bearing capacity of the tunnel entrance was taken as the damage criterion. The vulnerability curves corresponding to various damage levels were further developed based on the stochastic data from the probabilistic vulnerability analysis. When the charge weight was 200 kg, the tunnel entrance exhibited slight or moderate damage, while the tunnel entrance suffered severe or even complete damage as the charge weight increased to 1000 kg. However, the tunnel entrance’s probability of complete damage was less than 10% when the TNT charge weight did not exceed 1000 kg.

Recent advances in hyperspectral remote sensing techniques, especially in the hyperspectral image classification techniques, have provided efficient support for recognizing and analyzing ground objects. To date, most of the existing classification techniques have been designed for ideal hyperspectral images and have verified their effectiveness on high-quality hyperspectral image datasets. However, in real applications, available hyperspectral images often contain varying degrees of image degradation. Whether or not the classification accuracy will be reduced due to degradation problems in input data, and how it will be reduced become interesting questions. In this paper, we explore the effects of degraded inputs in hyperspectral image classification including the five typical degradation problems of low spatial resolution, Gaussian noise, stripe noise, fog, and shadow. Seven representative classification methods are chosen from different categories of classification methods and applied to analyze the specific influences of image degradation problems. Experiments are carried out from the aspects of single-type synthetic image degradation and mixed-type real image degradation. Consistent results from synthetic and real-data experiments show that the effects of degraded hyperspectral data in classification are related to image features, degradation types, degradation degrees, and the characteristics of classification methods. This provides constructive information for method selection in real applications where high-quality hyperspectral data are difficult to obtain and encourages researchers to develop more stable and effective classification methods for degraded hyperspectral images.

Reef islands are valuable terrestrial resources for ocean exploitation and utilization but face the serious threat of earthquake disasters. This study takes Zhubi Reef in the South China Sea as the research object and establishes a three-dimensional (3D) reef-seawater model to investigate the seismic response of Zhubi Reef. The 3D local topography and the effect of fluid-solid interaction are comprehensively considered. The artificial boundaries of fluid and solid domains are adopted to simulate the wave radiation and absorption of the semi-infinite seabed and the infinite seawater layer. The boundary substructure method is used to input the seismic waves into the numerical reef models. The results indicate that the seismic responses, including the peak values of the ground motions and the acceleration response spectra, are significantly amplified over Zhubi Reef. The acceleration response spectra on the reef flat shift closer towards the low-period direction compared with those of the seismic waves input from the subsea bedrock. In addition, the 3D topographic effect on the seismic response of Zhubi Reef is studied through a comparative analysis between two-dimensional (2D) and 3D reef models. The distribution laws of the peak response in the middle region of the reef flat calculated by the 2D and 3D models agree well with each other, and the differences are obvious in the edge areas where the local 3D topographies change drastically.

The traditional source-site-structure model for the calculation of seismic response of underground structures at near-source sites is restricted by the grid scale and the size of the structure. As a result, an excessive number of elements in the model make the numerical solving process difficult. To solve problems such as an inefficient computation and challenging nonlinear simulation, a multiscale analysis method for the calculation of the seismic response of underground structures at near-source sites is developed. The generalized free-field seismic response of the near-source region is obtained by establishing a large-scale calculation model of the source site and is used to simulate the fracture mechanism of faults and the process of seismic wave propagation. Then, using the method of seismic wave input based on artificial boundary substructures, the free-field motion of the wave is transformed into the equivalent seismic load, which is the seismic wave input data for the small-scale region of interest. Finally, with the help of local elements with special shapes to realize the grid transition of different scales, a small-scale model with reasonable soil-underground structure interaction is established, and the seismic response of the overall model can be effectively solved. The calculation and analysis of the seismic response of underground structures in irregular terrain are carried out. Compared with the results obtained directly from the source-site-structure model, the multiscale method has satisfactory accuracy and meets the needs of engineering design. Since the number of elements is fewer and the calculation time is much shorter than those required by the traditional model, the advantages in computational efficiency of the new method are highlighted. In addition, the reflected waves are too weak to have a considerable impact on structures because of the great energy loss at the reflection interface, which further proves the feasibility of the closed artificial boundary substructure method.

Hyperspectral image (HSI) is usually corrupted by various types of noise, including Gaussian noise, impulse noise, stripes, deadlines, and so on. Recently, sparse and low-rank matrix decomposition (SLRMD) has demonstrated to be an effective tool in HSI denoising. However, the matrix-based SLRMD technique cannot fully take the advantage of spatial and spectral information in a 3-D HSI data. In this paper, a novel group sparse and low-rank tensor decomposition (GSLRTD) method is proposed to remove different kinds of noise in HSI, while still well preserving spectral and spatial characteristics. Since a clean 3-D HSI data can be regarded as a 3-D tensor, the proposed GSLRTD method formulates a HSI recovery problem into a sparse and low-rank tensor decomposition framework. Specifically, the HSI is first divided into a set of overlapping 3-D tensor cubes, which are then clustered into groups by K-means algorithm. Then, each group contains similar tensor cubes, which can be constructed as a new tensor by unfolding these similar tensors into a set of matrices and stacking them. Finally, the SLRTD model is introduced to generate noisefree estimation for each group tensor. By aggregating all reconstructed group tensors, we can reconstruct a denoised HSI. Experiments on both simulated and real HSI data sets demonstrate the effectiveness of the proposed method.

Engineered cementitious composites (ECC) used as runway pavement material may suffer different strain rate loads such as aircraft taxiing, earthquakes, crash impacts, or blasts. In this paper, the dynamic tensile behaviors of the steel grid-polyvinyl alcohol (PVA) fiber and KEVLAR fiber-reinforced ECC were investigated by dynamic tensile tests at medium strain rates. The mixture was designed with different volume fractions of fibers and layer numbers of steel grids to explore the reinforcement effectiveness on the dynamic performance of the ECC. The volume fractions of these two types of fibers were 0%, 0.5%, 1%, 1.5%, and 2% of the ECC matrix, respectively. The layer numbers of the steel grid were 0, 1, and 2. The dynamic tensile behaviors of the PVA fiber and the KEVLAR fiber-reinforced ECC were also compared. The experimental results indicate that under dynamic tensile loads, the PVA-ECC reveals a ductile and multi-cracking failure behavior, and the KEVLAR-ECC displays a brittle failure behavior. The addition of the PVA fiber and the KEVLAR fiber can improve the tensile peak stress of the ECC matrix. For the specimens A0.5, A1, A1.5, and A2.0, the peak stress increases by 84.3%, 149.4%, 209.6%, and 237.3%, respectively, compared to the matrix specimen. For the specimens K0.5, K1, K1.5, and K2, the peak stress increases by about 72.3%, 147.0%, 195.2%, and 263.9%, respectively, compared to the matrix specimen. The optimum fiber volume content is 1.5% for the PVA-ECC and the KEVLAR-ECC. The KEVLAR-ECC can supply a higher tensile strength than the PVA-ECC, but the PVA-ECC reveals more prominent deformation capacity and energy dissipation performance than the KEVLAR-ECC. Embedding steel grid can improve the tensile peak stress and the energy dissipation of the ECC matrix. For the strain rate of 10−3 s−1, the peak stress of the A0.5S1 and A0.5S2 specimens increases by about 49.1% and 105.7% compared to the A0.5 specimen, and the peak stress of the K0.5S1 and K0.5S2 specimens increases by about 61.5% and 95.8%, respectively, compared to the K0.5 specimen.

Abstract Background Intraoperative blood pressure (BP) is a concern in daily clinic anesthesia and contributes to the differences in clinical outcome. We conducted a randomized controlled trial (RCT) to compare the effect of high vs. low mean arterial pressure (MAP) levels on clinical outcomes and complications in elderly patients under general anesthesia (GA). Methods In this multicenter, randomized, parallel-controlled, open-label, assessor-blinded clinical trial, 322 patients aged more than 65 years will be randomized for a low-level MAP (60–70 mmHg) or high-level MAP (90–100 mmHg) during non-cardiothoracic surgery under GA. The primary outcome will be the incidence of postoperative delirium. The secondary outcomes will include the delirium duration days, intraoperative urine volume, intraoperative blood loss, specific postoperative complications, and all-cause 28-day mortality. Discussion Results of this trial will help clarify whether BP management is beneficial for elderly patients under GA and will make clear whether the effect of high-level MAP can reduce the postoperative complication compared to low-level MAP. Trial registration ClinicalTrials.gov, NCT02857153 . Registered on 15 July 2016.

A continuous-wave 1.57 and 3.84 μm intracavity multiple optical parametric oscillator based on a single MgO:APLN crystal is reported for the first time. The synchronized dual-wavelength laser is obtained by using a folded-type double cavity, which consists of a 1064-nm resonator and a multiple optical parametric oscillator. With T = 10% at 1.47 and 3.3 μm output couplers, maximum output powers of 3.13 W at 1.57 μm and 0.85 W at 3.84 μm are obtained, corresponding to slope efficiencies of 6.8% and 1.9%, respectively. The power stabilities are better than 1.8% and 3% at the maximum output power in half an hour.

A new internal substructure method for seismic wave input in soil-structure systems was recently proposed. This method simplifies the calculation of equivalent input seismic loads and avoids the participation of artificial boundaries in the process of seismic wave input. However, in previous research and applications, the internal substructures are usually intercepted down from the free surface, which forms large substructures and increases the computational effort for data management on the substructure nodes, especially for deep underground structures. In this study, the internal substructure method is modified by intercepting the internal substructures entirely beneath the free surface and adjacently around the underground structures. Then, the equivalent input seismic loads are obtained through the dynamic analysis of the internal substructures and applied to the corresponding positions of the total soil-structure models. Thus, the earthquake energy can be more efficiently input into the region near the underground structures without losing computational accuracy. We provide the detailed implementation procedures of this modified method and validate its applicability and accuracy through the scattered problems of underground cavities in homogeneous and layered half-space sites.

Recently, superpixel segmentation has been proven to be a powerful tool for hyperspectral image (HSI) classification. Nonetheless, the selection of the optimal superpixel size is a nontrivial task. In addition, compared with single-scale superpixel segmentation, the same image segmented on a different scale can obtain different structure information. To overcome such a drawback also utilizing the structural information, a multiscale superpixel-based sparse representation (MSSR) algorithm for the HSI classification is proposed. Specifically, a modified segmentation strategy of multiscale superpixels is firstly applied on the HSI. Once the superpixels on different scales are obtained, the joint sparse representation classification is used to classify the multiscale superpixels. Furthermore, majority voting is utilized to fuse the labels of different scale superpixels and to obtain the final classification result. Two merits are realized by the MSSR. First, multiscale information fusion can more effectively explore the spatial information of HSI. Second, in the multiscale superpixel segmentation, except for the first scale, the superpixel number on a different scale for different HSI datasets can be adaptively changed based on the spatial complexity of the corresponding HSI. Experiments on four real HSI datasets demonstrate the qualitative and quantitative superiority of the proposed MSSR algorithm over several well-known classifiers.