On the Measurements of Individual Particle Properties Via Compression and Crushing

An experimental study is presented to measure the elastic, yielding, and crushing properties of individual particles under compression using substrates made of aluminum alloy, stainless steel, and sapphire. Carefully selected, highly spherical individual Ottawa sand particles of 0.75-1.1 mm in nominal diameter were compressed between two smooth substrates, and the load-deformation curves were analyzed by Hertz elastic contact theory to derive their reduced modulus and Young\u27s modulus as well as yielding and crushing strengths, which vary significantly with the type of substrate materials. Further analysis of the yielding and plastic deformation at the particle-substrate contact shows that the yield strength or hardness of the substrate materials dominates the local contact behavior and hence affects the measured apparent yielding and crushing strengths. The two softer substrates (aluminum alloy and stainless steel) actually lead to underestimated apparent shear yield strengths of quartz particles by 60.4% and 54.2%, respectively, which are actually the yielding of substrates, while the true particle yielding occurs in the sapphire-particle contact. Moreover, the two softer substrates cause much overestimated crushing strengths of the quartz particles by 50.4% and 36.4%, respectively. Selection of inappropriate substrate materials and inappropriate interpretation of the particle-substrate contact can lead to significant errors in the measured yielding and crushing strengths. It is recommended that single particle compression testing uses substrates with yield strength greater than that of the tested particles and result interpretation also considers the elastic and yielding behaviors of the substrates. (C) 2021 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences. Production and hosting by Elsevier B.V

Privacy-aware secure anonymous communication protocol in CPSS cloud computing

Cloud computing has emerged as a promising paradigm for the Internet of Things (IoT) and Cyber-Physical-Social Systems (CPSS). However, the problem of how to ensure the security of data transmission and data storage in CPSS is a key issue to address. We need to protect the confidentiality and privacy of users’ data and users’ identity during the transmission and storage process in CPSS. In order to avoid users’ personal information leakage from IoT devices during the process of data processing and transmitting, we propose a certificateless encryption scheme, and conduct a security analysis under the assumption of Computational Diffie-Hellman(CDH) Problem. Furthermore, based on the proposed cryptography mechanism, we achieve a novel anonymous communication protocol to protect the identity privacy of communicating units in CPSS. In the new protocol, an anonymous communication link establishment method and an anonymous communication packet encapsulation format are proposed. The Diffie-Hellman key exchange algorithm is used to construct the anonymous keys distribution method in the new link establishment method. And in the new onion routing packet encapsulation format, the session data are firstly separated from the authentication data to decrease the number of cryptography operations. That is, by using the new onion routing packet we greatly reduces the encryption operations and promotes the forwarding efficiency of anonymous messages, implementing the privacy, security and efficiency in anonymous communication in cyber-physical-social systems

Study on rainfall infiltration characteristic parameters of unsaturated soil

The study of the rainfall infiltration mechanism of unsaturated soil has always been a hot issue in the field of geotechnical engineering. It is worth studying which parameters should be introduced to characterize the infiltration characteristics of unsaturated soil in the calculation and analysis of rainfall infiltration. In this paper, the Fredlund–Xing model was quoted in the SEEP/W module of the Geostudio software, and the transient numerical calculation of rainfall infiltration under the same rainfall duration T and different rainfall intensity I was carried out for a soil column. Three infiltration characteristic parameters were introduced: rainfall infiltration front depth WF, suction reduction depth MRn, and section infiltration rate IR. The variation of these three parameters and rainfall intensity I during rainfall were sorted out and analyzed; it is indicated that WF increases with the extension of rainfall duration. MRn decreases with the increase of suction reduction rate n%, and when the rainfall duration is 24 h, the maximum depth of the soil column affected by rainfall is approximately 35% of the total depth. IR is mainly affected by the rainfall intensity I and the saturation permeability coefficient ks. There is a limit value for the influence of I on WF, MRn, and IR, and the limit rainfall intensity under the calculation conditions in this paper is I = 2.5ks

Preparation of Broad-Spectrum Polyclonal Antibody and Development of an Indirect Competitive-Enzyme Linked Immunosorbent Assay for Multi-Residue Detection of Biphenyl Tetrazolium Sartans in Antihypertensive Health Foods

An indirect competitive-enzyme linked immunosorbent assay (ic-ELISA) was established to detect the multi-residue of biphenyl tetrazolium sartans in antihypertensive health foods. Candesartan was coupled with bovine serum albumin to obtain immunogen. New Zealand white rabbits were immunized and a broad-spectrum antibody was obtained by an antibody screening assay. The half maximal inhibitory concentrations (IC50) for candesartan, losartan carboxylic acid, losartan potassium, olmesartan, olmesartan medoxomil, irbesartan, valsartan and valsartan methyl ester were 0.2, 0.2, 0.7, 0.04, 0.6, 0.3, 0.9 and 2.4 ng/mL, respectively. The samples were extracted with methanol and the matrix effect was eliminated by diluting the extract with standard solutions. The average recoveries of the eight target compounds were in the range from 80.6% to 120.0% with coefficients of variation equal to or below 14.0%. The results of ic-ELISA were highly correlated with those of liquid chromatography-tandem mass spectrometry (LC-MS/MS) (r > 0.97), indicating high accuracy and good reliability of ic-ELISA

IEF-CSNET: Information Enhancement and Fusion Network for Compressed Sensing Reconstruction

The rapidly growing requirement for data has put forward Compressed Sensing (CS) to realize low-ratio sampling and to reconstruct complete signals. With the intensive development of Deep Neural Network (DNN) methods, performance in image reconstruction from CS measurements is constantly increasing. Currently, many network structures pay less attention to the relevance of before- and after-stage results and fail to make full use of relevant information in the compressed domain to achieve interblock information fusion and a great receptive field. Additionally, due to multiple resamplings and several forced compressions of information flow, information loss and network structure redundancy inevitably result. Therefore, an Information Enhancement and Fusion Network for CS reconstruction (IEF-CSNET) is proposed in this work, and a Compressed Information Extension (CIE) module is designed to fuse the compressed information in the compressed domain and greatly expand the receptive field. The Error Comprehensive Consideration Enhancement (ECCE) module enhances the error image by incorporating the previous recovered error so that the interlink among the iterations can be utilized for better recovery. In addition, an Iterative Information Flow Enhancement (IIFE) module is further proposed to complete the progressive recovery with loss-less information transmission during the iteration. In summary, the proposed method achieves the best effect, exhibits high robustness at this stage, with the peak signal-to-noise ratio (PSNR) improved by 0.59 dB on average under all test sets and sampling rates, and presents a greatly improved speed compared with the best algorithm

Slope Detection Based on Grating Sensors

Because slope detection is often subject to various electromagnetic disturbances and weather limitations, there is an urgent need to develop a monitoring system that has the advantages of strong anti-electromagnetic interference capability, long service life, and small external environmental impact.Based on this demand, a real-time slope monitoring system based on fiber Bragg grating angle, crack, and osmotic pressure sensors is designed.The design idea is to use the sensitivity of FBG to the external stress, and the angle and crack penetrating pressure information are obtained from the offset of wavelength in different sensors to determine the stability of the slope.The design and implementation of the FBG slope monitoring system can better solve the existing problems in the slope monitoring, improve the ability to predict dangers, and strengthen the safety monitoring capability.The utility value of the system is high.</p

EiCSNet: Efficient Iterative Neural Network for Compressed Sensing Reconstruction

The rapid growth of sensing data demands compressed sensing (CS) in order to achieve high-density storage and fast data transmission. Deep neural networks (DNNs) have been under intensive development for the reconstruction of high-quality images from compressed data. However, the complicated auxiliary structures of DNN models in pursuit of better recovery performance lead to low computational efficiency and long reconstruction times. Furthermore, it is difficult for conventional neural network designs to reconstruct extra-high-frequency information at a very low sampling rate. In this work, we propose an efficient iterative neural network for CS reconstruction (EiCSNet). An efficient gradient extraction module is designed to replace the complex auxiliary structures in order to train the DNNs more efficiently. An iterative enhancement network is applied to make full use of the limited information available in CS for better iterative recovery. In addition, a frequency-aware weighted loss is further proposed for better image restoration quality. Our proposed compact model, EiCSNet2*1, improved the performance slightly and was nearly seven times faster than its counterparts, which shows that it has a highly efficient network design. Additionally, our complete model, EiCSNet6*1, achieved the best effect at this stage, where the average PSNR was improved by 0.37 dB for all testing sets and sampling rates

EiCSNet: Efficient Iterative Neural Network for Compressed Sensing Reconstruction

The rapid growth of sensing data demands compressed sensing (CS) in order to achieve high-density storage and fast data transmission. Deep neural networks (DNNs) have been under intensive development for the reconstruction of high-quality images from compressed data. However, the complicated auxiliary structures of DNN models in pursuit of better recovery performance lead to low computational efficiency and long reconstruction times. Furthermore, it is difficult for conventional neural network designs to reconstruct extra-high-frequency information at a very low sampling rate. In this work, we propose an efficient iterative neural network for CS reconstruction (EiCSNet). An efficient gradient extraction module is designed to replace the complex auxiliary structures in order to train the DNNs more efficiently. An iterative enhancement network is applied to make full use of the limited information available in CS for better iterative recovery. In addition, a frequency-aware weighted loss is further proposed for better image restoration quality. Our proposed compact model, EiCSNet2*1, improved the performance slightly and was nearly seven times faster than its counterparts, which shows that it has a highly efficient network design. Additionally, our complete model, EiCSNet6*1, achieved the best effect at this stage, where the average PSNR was improved by 0.37 dB for all testing sets and sampling rates

A Trans-Scale Study on the Influence of Water Content and Particle Size on Matric Suction

Exploring the water retention properties of unsaturated soil from the perspective of a liquid bridge has been a popular issue in recent years. This study first measures the soil–water characteristic curves (SWCCs) of granular specimens to determine the influence of particle size on matric suction from a macroscopic perspective. Then, the internal mechanism of the influence of particle size and volumetric water content on matric suction is analyzed from the mesoscopic perspective by using the Young–Laplace (Y–L) equation to calculate matric suction between two equal spheres. The macroscopic and mesoscopic experiments both show that matric suction decreases with an increase in particle radius. Moreover, identifying the internal mechanism of SWCC from the liquid bridge perspective is only applicable when the influence of gravity can be disregarded or is in the transitional stage. The influence of volumetric water content and sphere radius on matric suction is mostly caused by the variation in the outer radius of the liquid bridge (r1) and the neck radius of the liquid bridge (r2). With an increase in volumetric water content and sphere diameter, the increasing rate of r1 is much higher than r2, and the macroscopical matric suction gradually decreases