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Non-line-of-sight (NLOS) imaging is a novel radar sensing technology that enables the reconstruction of hidden targets. However, it may suffer from synthetic aperture length reduction caused by ambient occlusion. In this study, a complex total variation (CTV) regularization-based sparse reconstruction method for NLOS three-dimensional (3-D) imaging by millimeter-wave (mmW) radar, named RM-CSTV method, is proposed to improve imaging quality and speed. In this scheme, the NLOS imaging model is first introduced, and associated geometric constraints for NLOS objects are established. Second, an effective high-resolution NLOS imaging method based on the range migration (RM) kernel and complex sparse joint total variation constraint, dubbed as modified RM-CSTV, is proposed for 3-D high-resolution imaging with edge information. The experiments with multi-type NLOS targets show that the proposed RM-CSTV method can provide effective and high-resolution NLOS targets 3-D imaging.

A planar wideband circularly polarized (CP) end-fire antenna is presented for millimeter-wave (mm-wave) applications. First, the antenna consists of an open-ended substrate integrated waveguide (SIW) feeding line in the middle, a pair of stacked substrates with shorted triangular patches on both upper and lower sides, and an offset parallel strip. The shorted triangular patch is viewed as a horizontally polarized electric dipole, while the open-ended SIW is equivalent to an enhanced magnetic dipole due to the stacked substrates. Wide axial ratio (AR) performance is achieved by simultaneously exciting the two dipoles with orthogonal phase supplied by the offset parallel strip. Then, working principle and parameter analysis are explained and discussed for a further understanding. Finally, a prototype is fabricated and measured to verify the design scheme, which demonstrates wide AR bandwidth performance of 38.6%, novel and efficient radiating structure, simple and compact antenna size, and easy fabrication and integration. This indicates that the proposed antenna is a promising candidate to design wideband end-fire CP antenna in the mm-wave frequency band.

The injection from a well to other wells can be difficult in extralow-permeability oil reservoirs. In order to address this issue, a method of cyclic huff-n-puff with surfactants based on complex fracture networks for a single horizontal well was proposed and then investigated in terms of the effects of injection and fracture parameters on the oil recovery in water-wet extralow-permeability models. Firstly, the interfacial tension (IFT) and contact angle with different surfactant concentrations were measured to determine the basic properties of the surfactants. Then, the experiments of huff-n-puff with surfactants at different threshold injection pressures and soaking time were carried out to determine the oil increasing effects and analyze the pore-scale (micropores, mesopores, and macropores) mechanisms by combining the technology of nuclear magnetic resonance (NMR), which showed that the recovery increased with threshold injection pressure mostly in mesopores and macropores, while that increased with soaking time mostly in micropores. Eventually, the experiments of cyclic huff-n-puff based on different fracture distributions were conducted in six plate-fractured models to investigate the effects of surfactants, primary fracture, and secondary fracture on each cycle of huff-n-puff. Cyclic huff-n-puff with surfactants assisted by complex fracture networks including both primary and secondary fractures would bring to a higher oil recovery. However, other methods should be taken after several cycles of huff-n-puff due to the rapid reduction of oil recovery of each cycle. The findings for the proposed method should provide a meaningful guide to the development of extralow-permeability oil reservoirs.

With emphasis on long-span bridges, the dynamic responses of bridges without considering random traffic flows were found to be different from actual situations. The introduction of a random traffic flow model provides a new approach for the random analysis of bridge structure responses under vehicle loads. In this paper, the finite element and intelligent ant colony optimization-back propagation neural network (ACO-BPNN) models were used to study the dynamic responses of long-span bridges. The computational model was also validated by an experimental test. To confirm the validity of the proposed ACO-BPNN model after parameter selection, it was compared with the traditional back propagation neural network (BPNN) model and the genetic algorithm-back propagation neural network (GA-BPNN) model. BPNN, GA-BPNN, and ACO-BPNN adopt the same network topology structure to predict the dynamic responses of the long-span bridge. When the ACO-BPNN model conducted the iteration to the 130th generation, a training error of 0.009 was found to be smaller than the set critical error. In this manner, the computational accuracy was increased, and the optimized time was reduced. In addition, only 0.4 hours were spent in using the proposed ACO-BPNN model to predict the dynamic response of the long-span bridge. In the case of the same computer performance, it took 4.5 h to use the finite element model to predict the dynamic response of the long-span bridge. The advantage of the proposed ACO-BPNN model in predicting the performance of large-scale complex structures such as long-span bridges was clearly found.

143Nd/144Nd isotope ratio is the key for Sm-Nd isotope method. The 143Nd/144Nd isotope ratio of geological samples varies within a small range, and thus the 143Nd/144Nd isotope ratio analysis must be precise. In order to obtain high precision and high accuracy of the analytical data, the analysis process involving Nd isotope reference material plays an important role. The previous Nd isotope reference material is neodymium oxide, which have been exhausted in recent three decades. A national reference material of 143Nd/144Nd isotope ratio was introduced in this paper, which shows sufficient homogeneity and stability. The certified value of this reference material was determined by eleven participating laboratories using MC-TIMS and MC-ICP-MS. The 143Nd/144Nd isotope ratio value for the reference material is 0.512438, with a combined expanded uncertainty (k=2) of 5×10-6. The standard solution gets certification in May, 2015 and the Serial number is GSB 04-3258—2015. The standard solution can be used for quality monitoring of the equipment calibration and analysis process during the 143Nd/144Nd ratio determination for geology, resources, ocean, environment, archaeological and other type samples.

The random traffic flow model which considers parameters of all the vehicles passing through the bridge, including arrival time, vehicle speed, vehicle type, vehicle weight, and horizontal position as well as the bridge deck roughness, is input into the vehicle-bridge coupling vibration program. In this way, vehicle-bridge coupling vibration responses with considering the random traffic flow can be numerically simulated. Experimental test is used to validate the numerical simulation, and they had the consistent changing trends. This result proves the reliability of the vehicle-bridge coupling model in this paper. However, the computational process of this method is complicated and proposes high requirements for computer performance and resources. Therefore, this paper considers using a more advanced intelligent method to predict vibration responses of the long-span bridge. The PSO-BP (particle swarm optimization-back propagation) neural network model is proposed to predict vibration responses of the long-span bridge. Predicted values and real values at each point basically have the consistent changing trends, and the maximum error is less than 10%. Hence, it is feasible to predict vibration responses of the long-span bridge using the PSO-BP neural network model. In order to verify advantages of the predicting model, it is compared with the BP neural network model and GA-BP neural network model. The PSO-BP neural network model converges to the set critical error after it is iterated to the 226th generation, while the other two neural network models are not converged. In addition, the relative error of predicted values using PSO-BP neural network is only 2.71%, which is obviously less than the predicted results of other two neural network models. We can find that the PSO-BP neural network model proposed by the paper in predicting vibration responses is highly efficient and accurate.

First-principles calculations have been used to investigate the effects of Co addition on the preferred site occupation and magnetic properties of Ni-Fe-Ga-Co ferromagnetic shape memory alloys. The formation energy results indicate that the excess Ni constituent preferentially occupies the Fe sites in the off-stoichiometric Ni-Fe-Ga ternary alloy. The added Co tends to take the normal-Ni sites in the Ni-Fe-Ga-Co quaternary alloy during composition adjustment process. The total magnetic moment increases with Co content of the Ni36-xFe12Ga16Cox (x=0, 1, 2, 3 and 4) alloys. The difference between the up and down electronic density of states at the Fermi level gives rise to the increased magnetic property.

The overall three- dimensional assembly models of harmonic gear drive with double- circular- arc tooth profile and elliptical wave generator is established,then,the finite element analysis of the assembly model is carried out. Some improvements of the flexspline structure including fillet radius on flexible gear tooth and cut on smooth cylindrical outer surface of the flexible wheel is carried out on the basic structure,on The stress distribution and stress concentration of the flexspline are compared and analyzed. And the variation condition of the flexspline stress with the size of gear tooth fillet radius and the cut into the depth of arc of e flexible wheel smooth cylindrical outer surface is analyzed.

The booming development of customised e-commerce makes e-commerce supply chains experience a severe test of consumer service level (CSL). An efficient e-commerce supply chain resilience optimisation method addressing economic performance and service performance is proposed to improve customised services under disruption risks. The proposed method includes a hybrid strategy considering both a resistance strategy with blockchain (BCT) adoption and time-dependent recovery strategies simultaneously. A two-stage multi-period multi-product stochastic programming with BCT adoption is then proposed, which (1) implements BCT as the resistance strategy in the pre-disruption stage; (2) collaborates four recovery strategies in the post-disruption stage; (3) supports the e-tailer in making decisions and optimises his profit and order fulfillment time while taking product priorities into account. Using the actual data of Chinese e-commerce during the public health emergency in 2020, it is demonstrated that (1) the applicability of the model with BCT adoption in both supply chain resilience and CSL improvement; (2) the performance of the hybrid strategy in long-term disruption management; (3) the efficiency and robustness of the proposed solution approach for multi-objective high-dimensional stochastic programming problems. E-tailers can optimise decision-making under long-term disruptions by the proposed methodology in practices. [ABSTRACT FROM AUTHOR]

With the fierce competition in e-commerce, e-tailers are required to rapid responses to a variety of customised orders with multiple frequencies and strict delivery times. The delay or insufficient supply caused by disruptions might result in lost sales during long-term processes. To address this problem, a two-stage stochastic programming model considering profits, consumer service level (CSL) as well as market priorities is developed to manage long-term disruptions. We analyse multi-period consumer transaction data and formulate geographical relevance (GR) to link each marketplace with historical data in related regions and then prioritise market segments. A GR-based two-stage stochastic programming with multi-period is proposed, which (1) considers both proactive mitigation decisions before disruption and reactive recovery plans after disruption; (2) collaborates three resilience strategies; (3) optimises the e-tailer's profits considering market priorities during long-term disruptions. Using a real case of Chinese e-commerce under the COVID-19 pandemic, it is illustrated (1) the applicability and performance of the proposed GR-based model for multi-period resilience optimisation improving both the CSL and the total profit; (2) the efficiency and robustness of the developed sequential particle swarm optimisation with social structures algorithm. The proposed method could optimise e-tailers' response strategies for managing long-term disruptions in practice. [ABSTRACT FROM AUTHOR]