Financial Competitiveness of Macau in Comparison with Other Gaming Destinations

This paper analyzes the financial competitiveness of the Macau gaming industry visa- vis its counterparts in North America and Europe. The analysis covers casino product structure, revenue composition, assets productivity and financial returns of Macau versus those of gaming destinations in North America and Europe. The findings reveal that while Macau is advantageously positioned in terms of assets productivity and financial returns, its casino product structure and revenue composition seem at odds with today\u27s gaming trend. Macau is facing challenges from emerging competitors in Asia. To maintain a stable gaming revenue growth and retain its competitiveness, Macau must modify its casino product structure and revenue composition. Pursuing a more diversified market is a critical step towards the goal

SNARE based peptide linking as an efficient strategy to retarget botulinum neurotoxin’s enzymatic domain to specific neurons using diverse neuropeptides as targeting domains

Many disease states are caused by miss-regulated neurotransmission. A small fraction of these diseases can currently be treated with botulinum neurotoxin type A (BoNT/A). BoNT/A is composed of three functional domains – the light chain (Lc) is a zinc metalloprotease that cleaves intracellular SNAP25 which inhibits exocytosis, the translocation domain (Td) that enables the export of the light chain from the endosome to the cytosol, and the receptor binding domain (Rbd) that binds to extracellular gangliosides and synaptic vesicle glycoproteins while awaiting internalisation [1]. Current endeavours are directed towards retargeting Bont/A as well as finding safer methods of preparation and administration. Recently, our laboratory has developed a SNARE based linking strategy to recombine non-toxic BoNT/A fragments into a functional protein by simple mixing [2]. This SNARE based linking strategy permits the stepwise assembly of highly stable macromolecular complexes [2,3]. Onto these three SNARE peptides, diverse functional groups can be attached to the N- or C- terminus by direct synthesis and/or by genetic design. To enhance the therapeutic potential of BoNT/A, this method enables the rapid assembly of a large array of neuropeptide-SNAREs to their cognate LcTd-SNARE. A substitution of the Rbd with various neuropeptide sequences permits a large throughput combinatorial assay of LcTd to target new cell types. In this study, we have fused LcTd to 3 different Synaptobrevin sequences; we also use a small protein staple, and 26 different Syntaxin-neuropeptide fusions (permitting the assay of 78 new chimeric LcTd proteins with modified targeting domains). These neuropeptides such as, but not exclusively, somatostatin (SS), vasoactive intestinal peptide, substance P, opioid peptide analogues, Gonadotropin releasing hormone, and Arginine Vasopressin, which natively function through G protein coupled receptors (GPCR) can undergo agonist induced internalisation upon activation. The ability of our new constructs, once endocytosed, to inhibit neurotransmitter release was tested on different neuronal cell lines with immunoblotting of endogenous SNAP25. This cleavage by Lc reflects the ultimate readout of the enzyme’s efficacy, which incorporates the cell surface binding, internalisation kinetics, translocation of the Lc to the cytosol, and finally the enzymatic cleavage of SNAP25. Internalisation of the toxins can also be monitored with confocal microscopy and FACS by the substitution of the staple peptide for a fluorescent homologue. Figure 1 shows that whole boNT/A (upper left) can have its Rbd replaced with SNARE peptides, which will fuse together to form highly stable chimeric proteins with an altered targeting domain (right). Figure 1 also shows 4 different neuropeptide synthaxins in complex, resolved on SDS-PAGE gel (bottom left lanes 1-4, boiled 1’-4’). Fig. 1. SNARE-linked botulinum neurotoxins used for the retargeting of Bont/A. 29

Load Forecasting Based Distribution System Network Reconfiguration-A Distributed Data-Driven Approach

In this paper, a short-term load forecasting approach based network reconfiguration is proposed in a parallel manner. Specifically, a support vector regression (SVR) based short-term load forecasting approach is designed to provide an accurate load prediction and benefit the network reconfiguration. Because of the nonconvexity of the three-phase balanced optimal power flow, a second-order cone program (SOCP) based approach is used to relax the optimal power flow problem. Then, the alternating direction method of multipliers (ADMM) is used to compute the optimal power flow in distributed manner. Considering the limited number of the switches and the increasing computation capability, the proposed network reconfiguration is solved in a parallel way. The numerical results demonstrate the feasible and effectiveness of the proposed approach.Comment: 5 pages, preprint for Asilomar Conference on Signals, Systems, and Computers 201

Chance-Constrained Day-Ahead Hourly Scheduling in Distribution System Operation

This paper aims to propose a two-step approach for day-ahead hourly scheduling in a distribution system operation, which contains two operation costs, the operation cost at substation level and feeder level. In the first step, the objective is to minimize the electric power purchase from the day-ahead market with the stochastic optimization. The historical data of day-ahead hourly electric power consumption is used to provide the forecast results with the forecasting error, which is presented by a chance constraint and formulated into a deterministic form by Gaussian mixture model (GMM). In the second step, the objective is to minimize the system loss. Considering the nonconvexity of the three-phase balanced AC optimal power flow problem in distribution systems, the second-order cone program (SOCP) is used to relax the problem. Then, a distributed optimization approach is built based on the alternating direction method of multiplier (ADMM). The results shows that the validity and effectiveness method.Comment: 5 pages, preprint for Asilomar Conference on Signals, Systems, and Computers 201

Deep Learning Training with Simulated Approximate Multipliers

This paper presents by simulation how approximate multipliers can be utilized to enhance the training performance of convolutional neural networks (CNNs). Approximate multipliers have significantly better performance in terms of speed, power, and area compared to exact multipliers. However, approximate multipliers have an inaccuracy which is defined in terms of the Mean Relative Error (MRE). To assess the applicability of approximate multipliers in enhancing CNN training performance, a simulation for the impact of approximate multipliers error on CNN training is presented. The paper demonstrates that using approximate multipliers for CNN training can significantly enhance the performance in terms of speed, power, and area at the cost of a small negative impact on the achieved accuracy. Additionally, the paper proposes a hybrid training method which mitigates this negative impact on the accuracy. Using the proposed hybrid method, the training can start using approximate multipliers then switches to exact multipliers for the last few epochs. Using this method, the performance benefits of approximate multipliers in terms of speed, power, and area can be attained for a large portion of the training stage. On the other hand, the negative impact on the accuracy is diminished by using the exact multipliers for the last epochs of training.Comment: Presented at: IEEE International Conference on Robotics and Biomimetics (ROBIO) 2019, Dali, China, December 2019. WINNER OF THE MOZI BEST PAPER IN AI AWAR