The application of blockchain has become a trend in the development of supply chain finance. Aiming to bridge the gap in the existing literature, this paper investigates a supply chain finance system based on blockchain technology which contains a manufacturer, a retailer and a financial institution and incorporates blockchain costs into the model. Firstly, this paper establishes a supply chain finance model based on blockchain technology and it presents a comparison with the process employed under the traditional model. Secondly, this paper establishes the revenue mathematical model of supply chain finance based on blockchain technology. Thirdly, the optimal decisions of each participant under centralized and decentralized decision-making are proved and obtained, respectively, and the influencing factors of the optimal decisions are analyzed. Finally, the conclusions are verified via simulations. This study finds that, when blockchain is used, the benefits of each participant in the chain are increased. In addition, centralized decision-making, which is more optimal in the traditional model, is also enhanced under blockchain. This paper demonstrates the superiority of blockchain-enabled supply chain finance in terms of model and revenue. This provides some suggestions for companies in the supply chain with regard to solving the problem of financing difficulties.

Three electrokinetic (EK) experiments were designed to study the remediation efficiency of Cr(III) by EK-oxidation method and to investigate the influence of complexes and pH buffer solution in EK-oxidation process. Sediments Cr (III) can be oxidized into Cr(VI) effectively by KMnO₄, but the oxidation efficiency is not proportional to the dose of oxidant, and chemical oxidation process leads to a bit lower buffering capacity of the sediment. Compared with the simple EK remediation, the removal efficiency of total Cr in the sediments increased 32.6% by EK-oxidation method, and the concentrations of total chromium in the sediment showed increasing trend from the cathode to the anode after EK-oxidation treatment. The pH control and KMnO₄ improved the removal of Cr(VI) and total Cr from the sediments. Due to stronger complexation action of citric acid with Cr(III) in sediments, citric acid in the catholyte obviously increased the removal rate of total Cr in sediments. Finally, the removal rate of total Cr from sediments reached 60.9% by adding complex and controlling the pH of cathode and anode pond solution on EK-oxidation processes.

In this paper, we study a relay-aided multiuser amplify-and-forward relaying system where a multi-antenna base station (BS) transmits downlink to multiple single-antenna mobile stations (MSs) via a multi-antenna relay station (RS). We propose a novel precoder design scheme by using the joint integer-forcing method to achieve high rate with the algebraic structure of lattice codes. Inspired by the concept of decoding the linear equations in compute-and-forward relay systems, the linear precoding matrix is generalized as an integer matrix that is approximate to the channel matrix thereby mitigating the multiuser interference (MUI) at the MSs. The power allocation optimization problem at the BS is taken into account to improve the performance of the scheme. The simulation results show the outstanding performance of the proposed scheme.

An approach of scaling and shifting constellation for MIMO lattice reduction(LR)‐aided precoding is proposed. The proposed scheme is beneficial to improve the performance in terms of both BER and ergodic capacity. The feedback is a Gaussian integer matrix in which the entries are preserved in a region with respect to modulo operation. Then, the amount of feedback information is reduced significantly. Simulation results show that the LR‐aided precoding scheme using the improved version of the constellation apparently improves system performance.

Reconfigurable antennas offer great advantages over traditional antennas in terms of physical size and bandwidth. A practical reconfigurable antenna, usually equipped with multiple switches, has a high real-time requirement for the optimization algorithm. In this letter, we therefore propose a self-adaptive induced mutation algorithm (SIMA) that has a fast convergence. SIMA first initializes a population using good point set and determines the “key switches” by analyzing the distribution of the switch states of lower standing-wave ratio in the evolution. The configurations of worse states are then induced to set up their “key switches.” Experiments on the optimization of a 39-switch reconfigurable antenna system at 50, 200, and 350 MHz demonstrate that the convergence rate of SIMA is at least 2.15 times that of the genetic algorithm.