•A deep learning approach was proposed for electromechanical admittance (EMA)-based damage quantification.•Raw EMA signatures were directly learned by using 2D convolutional neural networks ...(CNNs).•Input size on the effectiveness of approach was evaluated by developing two CNN models.•Proof of concept experiments were conducted on detecting mass loss damages in concrete structure.•Experimental results confirmed high accuracy and efficiency of the approach to tiny damages.
Deep learning approach using convolutional neural networks (CNNs) has ushered in numerous breakthroughs in image-based recognition field, but the electromechanical impedance/admittance (EMI/EMA)-based structural damage identification by CNN remains being refined. This paper proposed a deep learning approach for the raw EMA-based rapid damage quantification on concrete structure utilizing two-dimensional (2D) CNNs. In the approach, the EMA signatures are first split into multiple sub-range responses, among which corresponding to the maximum indices namely root mean square deviations (RMSDs) are selected to construct the input of CNNs for training, and then damage severity degree could be rapidly predicted. The proposed approach is verified through crossover experiments of detecting multiple mass loss damages on a cubic concrete structure. Effect of input size on the performance of the approach is also evaluated by developing different CNN models. Experimental results confirm that the proposed approach is of high accuracy and efficiency even to tiny damages, thus paving a promising way to the real-life monitoring for concrete structures.
•New 1D CNN approach for deep learning of raw electromechanical admittance (EMA) data.•Proof-of-concept experiment on a laboratory concrete specimen with tiny mass-loss damages.•Practical application ...of the approach on bolt-looseness identification on full-scaled structure.•Performance comparison via developing different CNN models and traditional neural network.•High accuracy and efficiency of the approach for concrete structural damage quantification.
Common damages in concrete materials and structures are usually in small sizes at initial stage, which induce small stiffness and mass loss being difficult to evaluate severity level merely depending on traditional electromechanical admittance (EMA, inverse of impedance) technique. In fact, several state-of-the-art computerized techniques have been incorporated with the EMA for automated evaluation of concrete damages. However, complicated data preprocessing still limits the efficiency of these techniques in terms of accuracy and computational cost. To this end, this paper proposed a novel deep learning approach using one-dimensional (1-D) convolutional neural networks (CNNs) for exploiting the raw EMA signatures to automatically identify tiny damages in concrete structures, which eliminated tedious data preprocessing for network training and testing. Two independent EMA databases measured by smart piezoelectric sensors were established based on proof-of-concept experiment of slight/severe mass-loss damage detection on a concrete cube, as well as practical application of bolt-looseness identification on a full-scaled shield tunnel segment assembled structure. For both tests, effect of varied dimensions of input data on the efficiency of CNN models were evaluated as well. The well-trained CNN model perfectly quantified the severity degrees of mass-loss and bolt-looseness damages, which demonstrated significant superiority than traditional back propagation neural network and the model with less dimensions of input data exhibited higher accuracy. Delightful results in this work potentially provided a potential paradigm of EMA data-driven tiny damage identification for real-life concrete infrastructures.
MBene, a layered metal boride similar to MXene, has garnered significant attention in recent times. Nevertheless, there is still a lack of comprehensive understanding regarding their structure and ...properties, necessitating further research and investigation. In this study, theoretical calculations were employed first to study the binding energy, band structures and density of states of three MBenes, namely MoB, MgB2, and ZrB2, providing insights into their electronic characteristics. In addition, open circuit voltage (OCV) and adsorption energy were also conducted using the three MBenes as cathode materials for zinc ion batteries, indicating their low ion migration barrier and potential in energy storage application. Subsequently, MoB, MgB2, and ZrB2 were experimentally synthesized using etching or exfoliation method and utilized as cathode materials for zinc ion batteries. Results demonstrates that the specific capacity of MoB is 60 mAh g-1 at 0.1 A g-1. Systematic investigation including kinetics simulation and ex situ XRD suggests the cation insertion mechanism of the MoB electrodes. Building upon these findings, attempts were made to enhance the performance of MoB through the incorporation of 1 T-MoS2 composites, urea molecular intercalation. Notably, the modified composite exhibited a specific capacity exceeding 150 mAh g-1 at 0.1 A g-1, with a stable Coulombic efficiency of 100% after 100 cycles. This study provides novel directions and insights for the research of MBene in the context of aqueous zinc-ion batteries.
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•Theoretical calculations indicate the low ion migration barrier of MBenes.•MoB, MgB2, and ZrB2 were synthesized and utilized as cathode materials.•Ex-situ XRD and kinetic simulations indicate the Zn2+ insertion mechanism of MoB.•Performance is enhanced via urea intercalation and 1 T-MoS2 composites.
The present study established a rabbit model of global cerebral ischemia using the ＇six-vessel＇ method,which was reperfused after 30 minutes of ischemia.Rabbits received intravenous injection of ...propofol at 5 mg/kg prior to ischemia and 20 mg/kg per hour after ischemia until samples were prepared.Results revealed that propofol inhibited serum interleukin-8,endothelin-1 and malondialdehyde increases and promoted plasma superoxide dismutase activity after cerebral ischemia/reperfusion.In addition,cerebral cortex edema was attenuated with little neuronal nuclear degeneration and pyknosis with propofol treatment.The cross-sectional area of neuronal nuclei was,however,increased following propofol treatment.These findings suggested that propofol could improve anti-oxidant activity and inhibit synthesis of inflammatory factors to exert a protective effect on cerebral ischemia/reperfusion injury.
Controlling thermal radiation is central in a range of applications including sensing, energy harvesting, and lighting. The thermal emission spectrum can be strongly modified through the ...electromagnetic local density of states (EM LDOS) in nanoscale-patterned metals and semiconductors. However, these materials become unstable at high temperature, preventing improvements in radiative efficiency and applications such as thermophotovoltaics. Here, we report stable high-temperature thermal emission based on hot electrons (>2000 K) in graphene coupled to a photonic crystal nanocavity, which strongly modifies the EM LDOS. The electron bath in graphene is highly decoupled from lattice phonons, allowing a comparatively cool temperature (700 K) of the photonic crystal nanocavity. This thermal decoupling of hot electrons from the LDOS-engineered substrate opens a broad design space for thermal emission control that would be challenging or impossible with heated nanoscale-patterned metals or semiconductor materials.
Oriented object detection is a practical and challenging task in remote sensing image interpretation. Nowadays, oriented detectors mostly use horizontal boxes as intermedium to derive oriented boxes ...from them. However, the horizontal boxes are inclined to get small Intersection-over-Unions (IoUs) with ground truths, which may have some undesirable effects, such as introducing redundant noise, mismatching with ground truths, and detracting from the robustness of detectors. In this article, we propose a novel anchor-free oriented proposal generator (AOPG) that abandons horizontal box-related operations from the network architecture. AOPG first produces coarse oriented boxes by a coarse location module (CLM) in an anchor-free manner and then refines them into high-quality oriented proposals. After AOPG, we apply a Fast Region-based Convolutional Neural Network (R-CNN) head to produce the final detection results. Furthermore, the shortage of large-scale datasets is also a hindrance to the development of oriented object detection. To alleviate the data insufficiency, we release a new dataset on the basis of our DIOR dataset and name it DIOR-R. Massive experiments demonstrate the effectiveness of AOPG. Particularly, without bells and whistles, we achieve the accuracy of 64.41%, 75.24%, and 96.22% mAP on the DIOR-R, DOTA, and HRSC2016 datasets, respectively. Code and models are available at https://github.com/jbwang1997/AOPG .
Prior studies highlight consumer behavior in social commerce from the perspective of relational exchange, while culture-driven aspects have been neglected. Given cultural uniqueness in China, this ...study develops a research model to explore the effects of key social commerce affordances on swift guanxi dimensions and subsequent purchase intention. Data from 450 agricultural product consumers in social commerce were used in PLS analysis for testing the proposed research model. The results indicate that interactivity, stickiness, and word of mouth exert positive effects on mutual understanding, reciprocal favor, and relationship harmony, to various degrees. In turn, swift guanxi dimensions are determinants of consumers’ purchase intention in social commerce.
In the past few years, object detection in remote sensing images has achieved remarkable progress. However, the detection of oriented and densely packed objects are still unsatisfactory due to the ...following spatial and feature misalignments. 1) Most two-stage oriented detectors only introduce an orientation regression branch in the detection head, while still leverage horizontal proposals for classification and regression. This inevitably results in the spatial misalignment problem between horizontal proposals and oriented objects. 2) The features used for classification are in fact extracted from the region proposals which have shifted to the final predictions via the regression branch. This leads to the feature misalignment problem between the classification and the localization tasks. In this article, we present a two-stage oriented object detection method, termed dual-aligned oriented detector (DODet), toward evading the aforementioned problems of spatial and feature misalignments. In DODet, the first stage is an oriented proposal network (OPN), which generates high-quality oriented proposals via a novel representation scheme of oriented objects. The second stage is a localization-guided detection head (LDH) that aims at alleviating the feature misalignment between classification and localization. Comprehensive and extensive evaluations on three benchmarks, including DIOR-R, DOTA, and HRSC2016, indicate that our method could obtain consistent and substantial gains compared with the baseline method. The source code is publicly available at https://github.com/yanqingyao1994/DODet .
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