Search

Abstract Stretchable neuromorphic optoelectronics present tantalizing opportunities for intelligent vision applications that necessitate high spatial resolution and multimodal interaction. Existing neuromorphic devices are either stretchable but not reconcilable with multifunctionality, or discrete but with low-end neurological function and limited flexibility. Herein, we propose a defect-tunable viscoelastic perovskite film that is assembled into strain-insensitive quasi-continuous microsphere morphologies for intrinsically stretchable neuromorphic vision-adaptive transistors. The resulting device achieves trichromatic photoadaptation and a rapid adaptive speed (

Abstract Challenges associated with stretchable optoelectronic devices, such as pixel size, power consumption and stability, severely brock their realization in high-resolution digital imaging. Herein, we develop a universal detachable interface technique that allows uniform, damage-free and reproducible integration of micropatterned stretchable electrodes for pixel-dense intrinsically stretchable organic transistor arrays. Benefiting from the ideal heterocontact and short channel length (2 μm) in our transistors, switching current ratio exceeding 106, device density of 41,000 transistors/cm2, operational voltage down to 5 V and excellent stability are simultaneously achieved. The resultant stretchable transistor-based image sensors exhibit ultrasensitive X-ray detection and high-resolution imaging capability. A megapixel image is demonstrated, which is unprecedented for stretchable direct-conversion X-ray detectors. These results forge a bright future for the stretchable photonic integration toward next-generation visualization equipment.

The cumulative deformation properties of subgrade soil under cyclic traffic loads are critical for optimizing pavement structure design and ensuring long-term highway structural performance. This study aims to investigate the coupling effect of freeze-thaw cycles and cyclic loads on the cumulative deformation behaviors and meso-structure of coarse-grained saline soil (CGSS) subgrade filling in high-cold areas. Dynamic triaxial tests and computed tomography (CT) scanning were conducted to analyze the CGSS under different working conditions. The research focused on the dynamic deformation development and damage evolution under varying freeze-thaw cycles and load amplitudes. The research results show that the cumulative deformation behavior of CGSS under cyclic loading is relatively sensitive to the freeze-thaw process. The cumulative dynamic strain increases as the freeze-thaw cycles, with a critical freeze-thaw cycle number of five. The stable cumulative dynamic strain curve exhibits clear three-stage characteristics when plotted in semi-log coordination, with critical loading cycles at 20 and 1,000. After 10–100 loading cycles, the cumulative strain curve quickly shows failure. The CGSS’s low density and pore regions greatly increase after a freeze-thaw cycle. The rise in dynamic stress amplitude notably affects the bonding between soil particles and crystalline salts. The coupling effect of the freeze-thaw cycle and dynamic activity exacerbates the deterioration of soil structure, resulting in variations in CT values within the scanning layer in the final state.

AbstractThis study determined the optimum number of water spray particles and the angle of the inner angle of the nozzle, simulated and studied the suppression effect of the water curtain system on roadway belt fires and toxic and harmful gases, and analysed the barrier effect of heat and smoke under the water curtain system using the flow field. The results show that the number of water curtain rows has a suppressive effect on the temperature and that increasing the number of rows has a better barrier effect on the hot smoke downstream of the roadway. From the velocity and temperature fields, it is clear that there is significant upward movement between the water mists of the water curtain system, which is caused by the water mists colliding with each other on the roadway floor. These jets are entrained into the water mists and merge to form an area of upward movement that acts as a 'barrier’ to prevent the fumes from spreading downstream of the water curtain. When the water curtain system is activated, it increases the concentration of component gases in the downstream space of the roadway and rapidly restores visibility to meet the requirements for safe evacuation.

Abstract A 1:10 scale model tunnel with a length, height and width of 9 m, 0.6 m and 0.8 m, respectively, was set up in this paper. A water curtain system was installed in the model to investigate the effect of water curtain systems on smoke flow and heat propagation. A reduced-scale experimental and theoretical study was carried out by varying the heat release rate of the fire source, the water curtain pressure, and the number of water curtain rows. A series of tests were carried out for various setups to quantify each mechanism of interaction between the water mist and hot smoke, to propose a method for qualitatively analysing water curtain systems blocking the propagation of heat radiation and the flow of smoke from combustion, and to propose a method for predicting heat fluxes. The study found that the pressure of the water curtain, the number of rows, and the heat release rate of the fire source all had an effect on the smoke blocking effect of the water curtain system. This effect decreased as the heat release rate of the fire source increased and increased significantly with the pressure of the water curtain and the number of rows. The smoke blocking effect was quantified using conservation of momentum by establishing a dimensionless parameter R to represent the ratio of water curtain momentum to smoke momentum, as well as the ratio of heat flux before and after the water curtain to represent the smoke blocking capacity $$\delta$$ δ of the water curtain. The smoke blockage rate $$\delta$$ δ ranges between 40 and 75%, and the smoke blockage rate increases as the momentum R increases. Finally, in tunnel fires, a predictive model for the attenuation of heat radiation by water curtains has been developed, providing theoretical support for the quantitative study of the smoke and thermal blockage effects of water curtains, which is beneficial to the protection of human life in confined spaces.

Nitrogen is a fundamental component for building amino acids and proteins, playing a crucial role in the growth and development of plants. Leaf nitrogen concentration (LNC) serves as a key indicator for assessing plant growth and development. Monitoring LNC provides insights into the absorption and utilization of nitrogen from the soil, offering valuable information for rational nutrient management. This, in turn, contributes to optimizing nutrient supply, enhancing crop yields, and minimizing adverse environmental impacts. Efficient and non-destructive estimation of crop LNC is of paramount importance for on-field crop management. Spectral technology, with its advantages of repeatability and high-throughput observations, provides a feasible method for obtaining LNC data. This study explores the responsiveness of spectral parameters to soybean LNC at different vertical scales, aiming to refine nitrogen management in soybeans. This research collected hyperspectral reflectance data and LNC data from different leaf layers of soybeans. Three types of spectral parameters, nitrogen-sensitive empirical spectral indices, randomly combined dual-band spectral indices, and “three-edge” parameters, were calculated. Four optimal spectral index selection strategies were constructed based on the correlation coefficients between the spectral parameters and LNC for each leaf layer. These strategies included empirical spectral index combinations (Combination 1), randomly combined dual-band spectral index combinations (Combination 2), “three-edge” parameter combinations (Combination 3), and a mixed combination (Combination 4). Subsequently, these four combinations were used as input variables to build LNC estimation models for soybeans at different vertical scales using partial least squares regression (PLSR), random forest (RF), and a backpropagation neural network (BPNN). The results demonstrated that the correlation coefficients between the LNC and spectral parameters reached the highest values in the upper soybean leaves, with most parameters showing significant correlations with the LNC (p < 0.05). Notably, the reciprocal difference index (VI6) exhibited the highest correlation with the upper-layer LNC at 0.732, with a wavelength combination of 841 nm and 842 nm. In constructing the LNC estimation models for soybeans at different leaf layers, the accuracy of the models gradually improved with the increasing height of the soybean plants. The upper layer exhibited the best estimation performance, with a validation set coefficient of determination (R2) that was higher by 9.9% to 16.0% compared to other layers. RF demonstrated the highest accuracy in estimating the upper-layer LNC, with a validation set R2 higher by 6.2% to 8.8% compared to other models. The RMSE was lower by 2.1% to 7.0%, and the MRE was lower by 4.7% to 5.6% compared to other models. Among different input combinations, Combination 4 achieved the highest accuracy, with a validation set R2 higher by 2.3% to 13.7%. In conclusion, by employing Combination 4 as the input, the RF model achieved the optimal estimation results for the upper-layer LNC, with a validation set R2 of 0.856, RMSE of 0.551, and MRE of 10.405%. The findings of this study provide technical support for remote sensing monitoring of soybean LNCs at different spatial scales.

Wearable and skin-like X-ray detectors need intrinsic flexibility and high sensitivity. Here Guo et al. develop a stretchable n-type polymer semiconductor with aligned nanofiber networks which achieve photosensitivity of 1.52×10^4 μC Gyair^-1 cm^−2 and low detection limit of 37.7 nGyair s^-1.

In electrical impedance tomography (EIT) detection of industrial two-phase flows, the Gauss-Newton algorithm is often used for imaging. In complex cases with multiple bubbles, this method has poor imaging accuracy. To address this issue, a new algorithm called the artificial bee colony–optimized radial basis function neural network (ABC-RBFNN) is applied to industrial two-phase flow EIT for the first time. This algorithm aims to enhance the accuracy of image reconstruction in electrical impedance tomography (EIT) technology. The EIDORS-v3.10 software platform is utilized to generate electrode data for a 16-electrode EIT system with varying numbers of bubbles. This generated data is then employed as training data to effectively train the ABC-RBFNN model. The reconstructed electrical impedance image produced from this process is evaluated using the image correlation coefficient (ICC) and root mean square error (RMSE) criteria. Tests conducted on both noisy and noiseless test set data demonstrate that the ABC-RBFNN algorithm achieves a higher ICC value and a lower RMSE value compared to the Gauss–Newton algorithm and the radial basis function neural network (RBFNN) algorithm. These results validate that the ABC-RBFNN algorithm exhibits superior noise immunity. Tests conducted on bubble models of various sizes and quantities, as well as circular bubble models, demonstrate the ABC-RBFNN algorithm’s capability to accurately determine the size and shape of bubbles. This outcome confirms the algorithm’s generalization ability. Moreover, when experimental data collected from a 16-electrode EIT experimental device is employed as test data, the ABC-RBFNN algorithm consistently and accurately identifies the size and position of the target. This achievement establishes a solid foundation for the practical application of the algorithm.

Currently, people pay more and more attention to road maintenance, and the traffic characteristics of vehicles play an important role in road quality evolution and maintenance decision, which commonly depends on the collection and analysis of traffic data. Nevertheless, the rationality of traffic data analysis and the scientificity of maintenance decision are deficient. This study carries out a research on the data fusion of multisource traffic data including toll data and video surveillance data. First, the information of vehicle type and axle load is acquired from the toll data, and the lane, speed and temporal information are obtained from the video surveillance data. A Bayesian method is used to train toll data and video surveillance data to recover missing data. The vehicle type distribution probabilities of traffic volume during different periods and speeds in different lanes are investigated. Next, the number of equivalent standard axle load (ESAL) at different lanes, time periods, and speeds are estimated based on the axle load conversion relationship between different vehicle types. Then the axle load spectrum and distribution characteristics of traffic in different sections, lanes, speeds, and time periods are analyzed. Finally, the comparison of rutting depth from the multisource data fusion and specification is carried out, and it shows an apparent difference (e.g., beyond 20%) when the lateral distribution in lanes is taken into account. Although the difference is less than 10% by considering vehicle speed and time periods, the time to reach the same value of rutting depth maybe more than 1 year. Therefore, it greatly affects accurate determination of preventive maintenance timing. As a whole, this study provides beneficial information for accurately understanding the preventive maintenance opportunities and making reasonable maintenance decisions.

The detection of crack defects on the walls of road tunnels is a crucial step in the process of ensuring travel safety and performing routine tunnel maintenance. The automatic and accurate detection of cracks on the surface of road tunnels is the key to improving the maintenance efficiency of road tunnels. Machine vision technology combined with a deep neural network model is an effective means to realize the localization and identification of crack defects on the surface of road tunnels. We propose a complete set of automatic inspectionmethods for identifying cracks on the walls of road tunnels as a solution to the problem of difficulty in identifying cracks during manual maintenance. First, a set of equipment applied to the real-time acquisition of high-definition images of walls in road tunnels is designed. Images of walls in road tunnels are acquired based on the designed equipment, where images containing crack defects are manually identified and selected. Subsequently, the training and validation sets used to construct the crack inspection model are obtained based on the acquired images, whereas the regions containing cracks and the pixels of the cracks are finely labeled. After that, a crack area sensing module is designed based on the proposed you only look once version 7 model combined with coordinate attention mechanism (CAYOLO V7) network to locate the crack regions in the road tunnel surface images. Only subimages containing cracks are acquired and sent to the multiscale semantic segmentation module for extraction of the pixels to which the cracks belong based on the DeepLab V3+ network. The precision and recall of the crack region localization on the surface of a road tunnel based on our proposed method are 82.4% and 93.8%, respectively. Moreover, the mean intersection over union (MIoU) and pixel accuracy (PA) values for achieving pixel-level detection accuracy are 76.84% and 78.29%, respectively. The experimental results on the dataset show that our proposed two-stage detection method outperforms other state-of-the-art models in crack region localization and detection. Based on our proposedmethod, the images capturedon the surface of a roadtunnel cancomplete crackdetectionat a speedof ten frames/second, and the detection accuracy can reach 0.25 mm, which meets the requirements for maintenance of an actual project. The designed CA-YOLO V7 network enables precise localization of the area to which a crack belongs in images acquired under different environmental and lighting conditions in road tunnels. The improved DeepLab V3+ network based on lightweighting is able to extract crack morphology in a given region more quickly while maintaining segmentation accuracy. The established model combines defect localization and segmentation models for the first time, realizing pixel-level defect localization and extraction on the surface of road tunnels in complex environments, and is capable of determining the actual size of cracks based on the physical coordinate system after camera calibration. The trainedmodel has high accuracy and can be extended and applied to embedded computing devices for the assessment and repair of damaged areas in different types of road tunnels. [ABSTRACT FROM AUTHOR]

Intrinsically stretchable organic field-effect transistors (STOFETs) that behave much like skin, have garnered widespread attention and demonstrated potential to more comfortable wearing electronics. Despite considerable efforts being exerted in stretchable hybrid polymeric semiconductors to achieve high-mobility and high-stretchability STOFETs, carbon nanotubes (CNTs) as ideal electrode materials, its interfacial quality seldom attracts research interests. Herein, we demonstrate a novel and general strategy on flexible CNT-based heterojunction electrodes by pentacene modification layer to enable high-performance and functional STOFETs. Pentacene transition layer can reduce the surface roughness and tune the heterogeneity of functional groups on the CNT thin film, as well as enhance the compatibility and van der Waals contact between CNT electrodes and organic semiconductors. The experimental results reveal that the carrier mobility of the resulting STOFETs increases by 2-3 times and the subthreshold slope decreases by 5 times compared with unmodified devices. The on-current/off-current ratio reaches nearly 107, which is approximately 1000 times as high as unmodified devices, and the highest value for stretchable organic transistors reported so far. Furthermore, STOFETs with heterojunction electrodes demonstrate better performance homogeneity and higher strain-independent capability. Noteworthy, these are the first intrinsically stretchable organic phototransistors (STOPTs) with superior photoswitching performances and high strain tolerance developed.

Copyright of Ingénierie des Systèmes d'Information is the property of International Information & Engineering Technology Association (IIETA) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)