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: Population & Control theory. The organization has 6636 authors who have published 18324 publications receiving 327384 citations. The organization is also known as: UM & UMAC.

Composite Monte Carlo decision making under high uncertainty of novel coronavirus epidemic using hybridized deep learning and fuzzy rule induction.

Abstract: In the advent of the novel coronavirus epidemic since December 2019, governments and authorities have been struggling to make critical decisions under high uncertainty at their best efforts. In computer science, this represents a typical problem of machine learning over incomplete or limited data in early epidemic Composite Monte-Carlo (CMC) simulation is a forecasting method which extrapolates available data which are broken down from multiple correlated/casual micro-data sources into many possible future outcomes by drawing random samples from some probability distributions. For instance, the overall trend and propagation of the infested cases in China are influenced by the temporal-spatial data of the nearby cities around the Wuhan city (where the virus is originated from), in terms of the population density, travel mobility, medical resources such as hospital beds and the timeliness of quarantine control in each city etc. Hence a CMC is reliable only up to the closeness of the underlying statistical distribution of a CMC, that is supposed to represent the behaviour of the future events, and the correctness of the composite data relationships. In this paper, a case study of using CMC that is enhanced by deep learning network and fuzzy rule induction for gaining better stochastic insights about the epidemic development is experimented. Instead of applying simplistic and uniform assumptions for a MC which is a common practice, a deep learning-based CMC is used in conjunction of fuzzy rule induction techniques. As a result, decision makers are benefited from a better fitted MC outputs complemented by min-max rules that foretell about the extreme ranges of future possibilities with respect to the epidemic.

Relations among attractiveness of endorsers, match‐up, and purchase intention in sport marketing in China

Abstract: Purpose – The purpose of this paper is to investigate the relationships between attractiveness of athlete endorsers, match‐up, and consumers' purchase intention embedded in the China context.Design/methodology/approach – The authors used a 3×2×2 between‐subject experimental design. Specifically, in this 12‐scenario study depicting a purchase experience they manipulated endorser attractiveness levels (high/middle/low), endorser‐product match‐up (high/low), and product type (to prevent single product bias).Findings – The results showed that no matter whether the attractiveness is high, middle, or low, the high endorser‐product match‐up could produce higher purchase intention than the low endorser‐product match‐up could. Moreover, the purchase intention generated by the high‐attractive endorser with low match‐up would be higher than that generated by low‐attractive endorser with high match‐up.Originality/value – This research demonstrates that endorsers' attractiveness, even compared to match‐up factor, and ...

Co single-atom anchored on Co3O4 and nitrogen-doped active carbon toward bifunctional catalyst for zinc-air batteries

Abstract: Highly efficient noble-metal-free electrocatalysts are urgently explored for high energy density and safe metal-air batteries. Herein, an ingenious Co-Co3O4@NAC is prepared for this purpose by anchoring Co single atom on both Co3O4 nanoparticle and nitrogen-doped active carbon (NAC), where synergistic interaction among Co atoms, Co3O4 particles and NAC plays a significant role for excellent oxygen reduction reaction (ORR) and oxygen evolution reaction (OER). Moreover, the primary zinc-air battery (ZAB) with the Co-Co3O4@NAC as a cathode catalyst shows a high open circuit voltage (OCV) of 1.449 V, a specific energy density of 721 mA h/g and a maximum power density of 164 mW/cm2. The rechargeable ZAB with this catalyst displays a low voltage gap of 0.773 V at 10 mA/cm2 and stable cycling performance. This work provides a novel tactic to design elaborate high-efficient and promising bifunctional catalysts with non-noble metal atom and metal oxide for metal-air batteries.

AmPEP: Sequence-based prediction of antimicrobial peptides using distribution patterns of amino acid properties and random forest.

Abstract: Antimicrobial peptides (AMPs) are promising candidates in the fight against multidrug-resistant pathogens owing to AMPs' broad range of activities and low toxicity. Nonetheless, identification of AMPs through wet-lab experiments is still expensive and time consuming. Here, we propose an accurate computational method for AMP prediction by the random forest algorithm. The prediction model is based on the distribution patterns of amino acid properties along the sequence. Using our collection of large and diverse sets of AMP and non-AMP data (3268 and 166791 sequences, respectively), we evaluated 19 random forest classifiers with different positive:negative data ratios by 10-fold cross-validation. Our optimal model, AmPEP with the 1:3 data ratio, showed high accuracy (96%), Matthew's correlation coefficient (MCC) of 0.9, area under the receiver operating characteristic curve (AUC-ROC) of 0.99, and the Kappa statistic of 0.9. Descriptor analysis of AMP/non-AMP distributions by means of Pearson correlation coefficients revealed that reduced feature sets (from a full-featured set of 105 to a minimal-feature set of 23) can result in comparable performance in all respects except for some reductions in precision. Furthermore, AmPEP outperformed existing methods in terms of accuracy, MCC, and AUC-ROC when tested on benchmark datasets.