Enhanced Strategies for Seismic Resilient Posttensioned Reinforced Concrete Bridge Piers: Experimental Tests and Numerical Simulations

Recent studies have proposed and investigated the use of unbonded posttensioned (PT) bars in bridge substructure systems to improve their self-centering behavior. However, the lateral loading resistance and self-centering capacities of reinforced concrete (RC) piers with PT bars (i.e., the posttensioned RC bridge piers: PRC piers) could be easily compromised by early crushing of the base compression toe during a seismic event. Hence, in order to enhance the pier base integrity, the present study proposes and experimentally investigates three simple strategies for enhancing the seismic-damage resistance of PRC piers based on the use of (1) a steel tube to encase the PRC pier’s end segment; (2) ultrahigh performance concrete at the PRC pier’s end segment; and (3) engineered cementitious composite mortar bed underneath the pier bottom. The performance of these three PRC piers was assessed by comparing them with a conventional PRC pier under cyclic loading and considering the damage evolution and the cyclic force-displacement response. The comparison shows that the proposed enhanced PRC solutions allow improving the seismic performance of the system sustaining large lateral drifts with good self-centering behavior. Moreover, based on the test results, finite element models accounting for PT force loss and the rocking characteristics were validated to reproduce the piers’ cyclic response, thus highlighting the importance of considering PT force loss during numerical simulations

Fast blind deblurring of QR code images based on adaptive scale control.

With the development of 5G technology, the short delay requirements of commercialization and large amounts of data change our lifestyle day-to-day. In this background, this paper proposes a fast blind deblurring algorithm for QR code images, which mainly achieves the effect of adaptive scale control by introducing an evaluation mechanism. Its main purpose is to solve the out-of-focus caused by lens shake, inaccurate focus, and optical noise by speeding up the latent image estimation in the process of multi-scale division iterative deblurring. The algorithm optimizes productivity under the guidance of collaborative computing, based on the characteristics of the QR codes, such as the features of gradient and strength. In the evaluation step, the Tenengrad method is used to evaluate the image quality, and the evaluation value is compared with the empirical value obtained from the experimental data. Combining with the error correction capability, the recognizable QR codes will be output. In addition, we introduced a scale control parameter to study the relationship between the recognition rate and restoration time. Theoretical analysis and experimental results show that the proposed algorithm has high recovery efficiency and well recovery effect, can be effectively applied in industrial applications

Fast restoration for out-of-focus blurred images of QR code with edge prior information via image sensing.

Out-of-focus blurring of the QR code is very common in mobile Internet systems, which often causes failure of authentication as a result of a misreading of the information hence adversely affects the operation of the system. To tackle this difficulty, this work firstly introduced an edge prior information, which is the average distance between the center point and the edge of the clear QR code images in the same batch. It is motivated by the theoretical analysis and the practical observation of the theory of CMOS image sensing, optics information, blur invariants, and the invariance of the center of the diffuse light spots. After obtaining the edge prior information, combining the iterative image and the center point of the binary image, the proposed method can accurately estimate the parameter of the out-of-focus blur kernel. Furthermore, we obtain the sharp image by Wiener filter, a non-blind image deblurring algorithm. By this, it avoids excessive redundant calculations. Experimental results validate that the proposed method has great practical utility in terms of deblurring quality, robustness, and computational efficiency, which is suitable for barcode application systems, e.g., warehouse, logistics, and automated production

Rapid detection of multi-QR codes based on multistage stepwise discrimination and a compressed mobilenet.

Poor real-time performance in multi-QR codes detection has been a bottleneck in QR code decoding based Internet-of-Things (IoT) systems. To tackle this issue, we propose in this paper a rapid detection approach, which consists of Multistage Stepwise Discrimination (MSD) and a Compressed MobileNet. Inspired by the object category determination analysis, the preprocessed QR codes are extracted accurately on a small scale using the MSD. Guided by the small scale of the image and the end-to-end detection model, we obtain a lightweight Compressed MobileNet in a deep weight compression manner to realize rapid inference of multi-QR codes. The Average Detection Precision (ADP), Multiple Box Rate (MBR) and running time are used for quantitative evaluation of the efficacy and efficiency. Compared with a few state-of-the-art methods, our approach has higher detection performance in rapid and accurate extraction of all the QR codes. The approach is conducive to embedded implementation in edge devices along with a bit of overhead computation to further benefit a wide range of real-time IoT applications

Rewritable and sustainable 2D barcode for traceability application in smart IoT based fault-tolerant mechanism.

With the development of the Internet of Things (IoT) technology, two-dimensional (2D) barcodes are widely used in smart IoT applications as a perception portal. In industries with many circulations and testing links like traceability, since the existing 2D barcode cannot be changed once it is printed, it can only be replaced with more expensive radio frequency identification (RFID) labels or new 2D barcodes, causing a waste of human resources and costs. For better circulation efficiency and resource utilization, we propose a new design of the rewritable and sustainable 2D barcode based on the fault-tolerance mechanism. The ability to add new information in the 2D barcode can be achieved through data encryption and the insertion of a rewritable layer. It means the message of 2D barcodes could be changed, and increases the flexibility and liquidity of the 2D barcode application. Besides, the encoding and decoding method of the proposed 2D barcode is presented. Experimental results have illustrated the superiority of rewritable and sustainable 2D barcodes in the traceability of herbal medicine compared with the conventional 2D barcodes, and demonstrated the feasibility of the design. The findings show the potential for significant application in the field of traceability in smart IoT, as well as in the manufacturing industry and logistics

Customized 2D Barcode Sensing for Anti-Counterfeiting Application in Smart IoT with Fast Encoding and Information Hiding

With the development of commodity economy, the emergence of fake and shoddy products has seriously harmed the interests of consumers and enterprises. To tackle this challenge, customized 2D barcode is proposed to satisfy the requirements of the enterprise anti-counterfeiting certification. Based on information hiding technology, the proposed approach can solve these challenging problems and provide a low-cost, difficult to forge, and easy to identify solution, while achieving the function of conventional 2D barcodes. By weighting between the perceptual quality and decoding robustness in sensing recognition, the customized 2D barcode can maintain a better aesthetic appearance for anti-counterfeiting and achieve fast encoding. A new picture-embedding scheme was designed to consider 2D barcode, within a unit image block as a basic encoding unit, where the 2D barcode finder patterns were embedded after encoding. Experimental results demonstrated that the proposed customized barcode could provide better encoding characteristics, while maintaining better decoding robustness than several state-of-the-art methods. Additionally, as a closed source 2D barcode that could be visually anti-counterfeit, the customized 2D barcode could effectively prevent counterfeiting that replicate physical labels. Benefitting from the high-security, high information capacity, and low-cost, the proposed customized 2D barcode with sensing recognition scheme provide a highly practical, valuable in terms of marketing, and anti-counterfeiting traceable solution for future smart IoT applications

A theoretical and numerical investigation of a family of immersed finite element methods

In this article we consider the widely used immersed finite element method (IFEM), in both explicit and implicit form, and its relationship to our more recent one-field fictitious domain method (FDM). We review and extend the formulation of these methods, based upon an operator splitting scheme, in order to demonstrate that both the explicit IFEM and the one-field FDM can be regarded as particular linearizations of the fully implicit IFEM. However, the one-field FDM can be shown to be more robust than the explicit IFEM and can simulate a wider range of solid parameters with a relatively large time step. In addition, it can produce results almost identical to the implicit IFEM but without iteration inside each time step. We study the effect on these methods of variations in viscosity and density of fluid and solid materials. The advantages of the one-field FDM within the IFEM framework are illustrated through a selection of parameter sets for two benchmark cases

Ecoenzymatic stoichiometry reveals widespread soil phosphorus limitation to microbial metabolism across Chinese forests

8 páginas.- 4 figuras.- 57 referencias.- Supplementary information The online version contains supplementary material available at https://doi.org/10.1038/s43247-022-00523-5Forest soils contain a large amount of organic carbon and contribute to terrestrial carbon sequestration. However, we still have a poor understanding of what nutrients limit soil microbial metabolism that drives soil carbon release across the range of boreal to tropical forests. Here we used ecoenzymatic stoichiometry methods to investigate the patterns of microbial nutrient limitations within soil profiles (organic, eluvial and parent material horizons) across 181 forest sites throughout China. Results show that, in 80% of these forests, soil microbes were limited by phosphorus availability. Microbial phosphorus limitation increased with soil depth and from boreal to tropical forests as ecosystems become wetter, warmer, more productive, and is affected by anthropogenic nitrogen deposition. We also observed an unexpected shift in the latitudinal pattern of microbial phosphorus limitation with the lowest phosphorus limitation in the warm temperate zone (41-42 degrees N). Our study highlights the importance of soil phosphorus limitation to restoring forests and predicting their carbon sinks. Phosphorus limitation of soil microbial communities in forests is widespread, increases with soil depth, and is enhanced under wetter and warmer climates and elevated anthropogenic nitrogen deposition, according to ecoenzymatic stoichiometric analyses across 181 forests in China.This study was financially supported by the Strategic Priority Research Program of Chinese Academy of Sciences (XDB40000000), Funds for International Cooperation and Exchange of National Natural Science Foundation of China (32061123007), National Natural Science Foundation of China (41977031), Program of State Key Laboratory of Loess and Quaternary Geology CAS (SKLLQGZR1803). Contributions from Dr. Chen are funded by H2020 Marie Skłodowska-Curie Actions (No. 839806). M.D.-B. acknowledges support from the Spanish Ministry of Science and Innovation for the I+D+i project PID2020-115813RA-I00 funded by CIN/AEI/10.13039/501100011033. M.D.-B. is also supported by a project of the Fondo Europeo de Desarrollo Regional (FEDER) and the Consejería de Transformación Económica, Industria, Conocimiento y Universidades of the Junta de Andalucía (FEDER Andalucía 2014-2020 Objetivo temático “01–Refuerzo de la investigación, el desarrollo tecnológico y la innovación”) associated with the research project P20_00879 (ANDABIOMA).Peer reviewe

Combination of Joint Representation and Adaptive Weighting for Multiple Features with Application to SAR Target Recognition

For the synthetic aperture radar (SAR) target recognition problem, a method combining multifeature joint classification and adaptive weighting is proposed with innovations in fusion strategies. Zernike moments, nonnegative matrix factorization (NMF), and monogenic signal are employed as the feature extraction algorithms to describe the characteristics of original SAR images with three corresponding feature vectors. Based on the joint sparse representation model, the three types of features are jointly represented. For the reconstruction error vectors from different features, an adaptive weighting algorithm is used for decision fusion. That is, the weights are adaptively obtained under the framework of linear fusion to achieve a good fusion result. Finally, the target label is determined according to the fused error vector. Experiments are conducted on the moving and stationary target acquisition and recognition (MSTAR) dataset under the standard operating condition (SOC) and four extended operating conditions (EOC), i.e., configuration variants, depression angle variances, noise interference, and partial occlusion. The results verify the effectiveness and robustness of the proposed method