University of Science and Technology Beijing

About: University of Science and Technology Beijing is a education organization based out in Beijing, China. It is known for research contribution in the topics: Microstructure & Alloy. The organization has 41558 authors who have published 44473 publications receiving 623229 citations. The organization is also known as: Beijing Steel and Iron Institute.

Distributed Continuous-Time Algorithms for Resource Allocation Problems Over Weight-Balanced Digraphs

Abstract: In this paper, a distributed resource allocation problem with nonsmooth local cost functions is considered, where the interaction among agents is depicted by strongly connected and weight-balanced digraphs. Here the decision variable of each agent is within a local feasibility constraint described as a convex set, and all the decision variables have to satisfy a network resource constraint, which is the sum of available resources. To solve the problem, a distributed continuous-time algorithm is developed by virtue of differentiated projection operations and differential inclusions, and its convergence to the optimal solution is proved via the set-valued LaSalle invariance principle. Furthermore, the exponential convergence of the proposed algorithm can be achieved when the local cost functions are differentiable with Lipschitz gradients and there are no local feasibility constraints. Finally, numerical examples are given to verify the effectiveness of the proposed algorithms.

Origin of metallic behavior in NiCo2O4 ferrimagnet.

Abstract: Predicting and understanding the cation distribution in spinels has been one of the most interesting problems in materials science. The present work investigates the effect of cation redistribution on the structural, electrical, optical and magnetic properties of mixed-valent inverse spinel NiCo2O4(NCO) thin films. It is observed that the films grown at low temperatures (T 400 °C) are insulators with lower ferrimagnetic-paramagnetic phase transition temperature. So far, n-type Fe3O4 has been used as a conducting layer for the spinel thin films based devices and the search for a p-type counterpart still remains elusive. The inherent coexistence and coupling of ferrimagnetic order and the metallic nature in p-type NCO makes it a promising candidate for spintronic devices. Detailed X-ray Absorption and X–ray Magnetic Circular Dichroism studies revealed a strong correlation between the mixed-valent cation distribution and the resulting ferrimagnetic-metallic/insulating behavior. Our study clearly demonstrates that it is the concentration of Ni3+ions and the Ni3+–O2−Ni2+ double exchange interaction that is crucial in dictating the metallic behavior in NCO ferrimagnet. The metal-insulator and the associated magnetic order-disorder transitions can be tuned by the degree of cation site disorder via growth conditions.

Irradiation Behavior in High Entropy Alloys

Abstract: As an increasing demand of advanced nuclear fission reactors and fusion facilities, the key requirements for the materials used in advanced nuclear systems should encompass superior high temperature property, good behavior in corrosive environment, and high irradiation resistance, etc. Recently, it was found that some selected high entropy alloys (HEAs) possess excellent mechanical properties at high temperature, high corrosion resistance, and no grain coarsening and self-healing ability under irradiation, especially, the exceptional structural stability and lower irradiation-induced volume swelling, compared with other conventional materials. Thus, HEAs have been considered as the potential nuclear materials used for future fission or fusion reactors, which are designed to operate at higher temperatures and higher radiation doses up to several hundreds of displacement per atom (dpa). An insight into the irradiation behavior of HEAs was given, including fundamental researches to investigate the irradiation-induced phase crystal structure change and volume swelling in HEAs. In summary, a brief overview of the irradiation behavior in HEAs was made and the irradiation-induced structural change in HEAs may be relatively insensitive because of their special structures.

Recent advances in microRNA detection

Abstract: The development of simple, robust, and reliable microRNA (miRNA) detection methods is of great significance in the studies of the biological function of miRNAs, molecular diagnostics, treatment of diseases, and targeted drugs. In recent years, with the increasing development of miRNA research, lots of novel approaches were developed for the detection of miRNA in terms of sensitivity, specificity, multiplicity, in situ imaging, etc. In particular, nucleic acid amplification-based methods and many detection techniques such as droplet digital PCR (ddPCR), electrochemiluminescence (ECL), surface-enhanced Raman spectroscopy (SERS), and mass spectrometry (MS) have been employed widely for the highly sensitive detection of miRNA. New progress in miRNA detection has accelerated miRNA functional research and clinical diagnostics. In this review, we summarize the recent progress in the development of miRNA detection methods and new applications. This review will provide guidelines for the development of more advanced miRNA detection methods with high sensitivity and specificity, and applicability to biochemical research, disease diagnosis and therapy.

Adaptive Dynamic Programming for a Class of Complex-Valued Nonlinear Systems

Abstract: In this brief, an optimal control scheme based on adaptive dynamic programming (ADP) is developed to solve infinite-horizon optimal control problems of continuous-time complex-valued nonlinear systems. A new performance index function is established on the basis of complex-valued state and control. Using system transformations, the complex-valued system is transformed into a real-valued one, which overcomes Cauchy-Riemann conditions effectively. With the transformed system and the performance index function, a new ADP method is developed to obtain the optimal control law by using neural networks. A compensation controller is developed to compensate the approximation errors of neural networks. Stability properties of the nonlinear system are analyzed and convergence properties of the weights for neural networks are presented. Finally, simulation results demonstrate the performance of the developed optimal control scheme for complex-valued nonlinear systems.