University of Macau

About: University of Macau is a education organization based out in Macao, Macau, China. It is known for research contribution in the topics: Computer science & Population. The organization has 6636 authors who have published 18324 publications receiving 327384 citations. The organization is also known as: UM & UMAC.

2′,7′-Dichlorodihydrofluorescein as a fluorescent probe for reactive oxygen species measurement: Forty years of application and controversy

Abstract: Reactive oxygen species (ROS) are critically important chemical intermediates in biological studies, due to their multiple physiologically essential functions and their often pathologically deleterious effects. Consequently, it is vital that their presence in biological samples has to be quantifiable. However, their high activity, very short life span and extremely low concentrations make ROS measurement a scientifically challenging subject for researchers. One of the widespread methods for ROS detection, based on the oxidation of the non-fluorescent probe 2',7'-dichlorodihydrofluorescein (DCFH(2)) to yield the highly fluorescent 2',7'-dichlorofluorescein (DCF), was developed more than 40 years ago. However, from its initial application, argumentative questions have arisen regarding its action mechanisms, reaction principles and especially its specificity. Herein, the authors attempt to undertake a comprehensive review: to describe the basic characteristics of DCFH(2); to discuss the present views of the mechanisms of its fluorescence formation; to summarize the fluorescence formation interferents; to outline its application in biological research; and to underline its advantages and disadvantages in ROS detection as well as for the methodological considerations that arise during analysis.

Genetic Learning Particle Swarm Optimization

Abstract: Social learning in particle swarm optimization (PSO) helps collective efficiency, whereas individual reproduction in genetic algorithm (GA) facilitates global effectiveness. This observation recently leads to hybridizing PSO with GA for performance enhancement. However, existing work uses a mechanistic parallel superposition and research has shown that construction of superior exemplars in PSO is more effective. Hence, this paper first develops a new framework so as to organically hybridize PSO with another optimization technique for “learning.” This leads to a generalized “learning PSO” paradigm, the *L-PSO. The paradigm is composed of two cascading layers, the first for exemplar generation and the second for particle updates as per a normal PSO algorithm. Using genetic evolution to breed promising exemplars for PSO, a specific novel *L-PSO algorithm is proposed in the paper, termed genetic learning PSO (GL-PSO). In particular, genetic operators are used to generate exemplars from which particles learn and, in turn, historical search information of particles provides guidance to the evolution of the exemplars. By performing crossover, mutation, and selection on the historical information of particles, the constructed exemplars are not only well diversified, but also high qualified. Under such guidance, the global search ability and search efficiency of PSO are both enhanced. The proposed GL-PSO is tested on 42 benchmark functions widely adopted in the literature. Experimental results verify the effectiveness, efficiency, robustness, and scalability of the GL-PSO.

Public Policy, Political Connections, and Effective Tax Rates: Longitudinal Evidence from Malaysia

Abstract: This study examines the link between effective tax rates (ETR) and political connections in developing economies The political connections explanation is informed by the observation that developing economies tend to be relationship-based rather than market-based capitalisms Two proxies of political patronage are developed and applied to a group of Malaysian firms over a 10-year period We find firms with political connections pay tax at significantly lower effective rates than other firms Our results suggest that political connections are an important determinant of ETR in relationship-based economies

Neural Network Control-Based Adaptive Learning Design for Nonlinear Systems With Full-State Constraints

Abstract: In order to stabilize a class of uncertain nonlinear strict-feedback systems with full-state constraints, an adaptive neural network control method is investigated in this paper. The state constraints are frequently emerged in the real-life plants and how to avoid the violation of state constraints is an important task. By introducing a barrier Lyapunov function (BLF) to every step in a backstepping procedure, a novel adaptive backstepping design is well developed to ensure that the full-state constraints are not violated. At the same time, one remarkable feature is that the minimal learning parameters are employed in BLF backstepping design. By making use of Lyapunov analysis, we can prove that all the signals in the closed-loop system are semiglobal uniformly ultimately bounded and the output is well driven to follow the desired output. Finally, a simulation is given to verify the effectiveness of the method.