ANALYZE THE TREATMENT REGIMENS AND THROMBOSIS PROPHYLAXIS USED IN CORONARY ARTERY INTERVENTION AT INTERVENTIONAL CARDIOLOGY UNIT IN CAN THO CENTRAL GENERAL HOSPITAL

Objective: The study was conducted to analyze the rationality of treatment regimens and thrombosis prophylaxis used in coronary artery intervention to compare to guidelines for treatment according to VNHA and recommendation of ACC/AHA at Interventional cardiology in Can Tho Central General Hospital. Methods: The cross-sectional study was based on the data collected from entire medical records of patients at Interventional cardiology in Can Tho Central General Hospital from August 2017 to February 2018. The rationality of the antithrombotic regimen used at the Hospital is assessed through criteria such as medical combination, dosage, time to take medicine, clinical trials during the treatment. Results: The study found that 95.6% and 90.7% were suitable for medical combination before and after PCI; 100% fit for the use of medicine; and 100% was suitable for antithrombotic agents and clinical trials during treatment time; in terms of dosage, the result showed that entrance and maintenance were 84.9% and 100% for aspirin respectively; 71.7% and 100% for clopidogrel; 100% and 94.7% for ticagrelor; 90.2-92.8% and 98.1% for enoxaparin; especially, heparin-100% anticoagulant was appropriate to recommend. Conclusion: The study showed that treatment regimens and thrombosis prophylaxis in percutaneous coronary intervention at Interventional cardiology in Can Tho Central General Hospital were quite suitable compared to the recommendations of the Heart Association. The results from the study are a scientific basis for the Hospital to maintain or consider adjustments to improve the quality of treatment, ensure the effectiveness and safety of patients

Robust unit commitment with n - 1 security criteria

The short-term unit commitment and reserve scheduling decisions are made in the face of increasing supply-side uncertainty in power systems. This has mainly been caused by a higher penetration of renewable energy generation that is encouraged and enforced by the market and policy makers. In this paper, we propose a two-stage stochastic and distributionally robust modeling framework for the unit commitment problem with supply uncertainty. Based on the availability of the information on the distribution of the random supply, we consider two specific models: (a) a moment model where the mean values of the random supply variables are known, and (b) a mixture distribution model where the true probability distribution lies within the convex hull of a finite set of known distributions. In each case, we reformulate these models through Lagrange dualization as a semi-infinite program in the former case and a one-stage stochastic program in the latter case. We solve the reformulated models using sampling method and sample average approximation, respectively. We also establish exponential rate of convergence of the optimal value when the randomization scheme is applied to discretize the semi-infinite constraints. The proposed robust unit commitment models are applied to an illustrative case study, and numerical test results are reported in comparison with the two-stage non-robust stochastic programming model

Achieving Better Regret against Strategic Adversaries

We study online learning problems in which the learner has extra knowledge about the adversary's behaviour, i.e., in game-theoretic settings where opponents typically follow some no-external regret learning algorithms. Under this assumption, we propose two new online learning algorithms, Accurate Follow the Regularized Leader (AFTRL) and Prod-Best Response (Prod-BR), that intensively exploit this extra knowledge while maintaining the no-regret property in the worst-case scenario of having inaccurate extra information. Specifically, AFTRL achieves $O(1)$ external regret or $O(1)$ \emph{forward regret} against no-external regret adversary in comparison with $O(\sqrt{T})$ \emph{dynamic regret} of Prod-BR. To the best of our knowledge, our algorithm is the first to consider forward regret that achieves $O(1)$ regret against strategic adversaries. When playing zero-sum games with Accurate Multiplicative Weights Update (AMWU), a special case of AFTRL, we achieve \emph{last round convergence} to the Nash Equilibrium. We also provide numerical experiments to further support our theoretical results. In particular, we demonstrate that our methods achieve significantly better regret bounds and rate of last round convergence, compared to the state of the art (e.g., Multiplicative Weights Update (MWU) and its optimistic counterpart, OMWU)

Mobile Services Meet Distributed Cloud: Benefits, Applications, and Challenges

As the explosive growth of smart devices and enormous new applications, the variety of corresponding cloud services has been growing quickly. The conventional centralized cloud was faced with an overhead on backhaul links and high latency. Accordingly, a decentralized cloud paradigm including edge computing, mobile edge computing, cloudlet, and so on, was introduced to distribute cloud services to the edge network which located in proximity to mobile devices few years ago. However, this paradigm was not paid attention at that time since cloud technology and mobile network communication were immature to motivate mobile services. Recently, with the overwhelming growth of mobile communication technology and cloud technology, distributed cloud is emerging as a paradigm well equipped with technologies to support a broad range of mobile services. The 5G mobile communication technology provides high-speed data and low latency. Cloud services can be automatically deployed in the edge networks quickly and easily. Distributed cloud can prove itself to bring many benefits for mobile service such as reducing network latency, as well as computational and network overhead at the central cloud. Besides, we present some applications to emphasize the necessity of distributed cloud for mobile service and discuss further technical challenges in distributed cloud

Finding and verifying the nucleolus of cooperative games

The nucleolus offers a desirable payoff-sharing solution in cooperative games, thanks to its attractive properties—it always exists and lies in the core (if the core is non-empty), and it is unique. The nucleolus is considered as the most ‘stable’ solution in the sense that it lexicographically minimizes the dissatisfactions among all coalitions. Although computing the nucleolus is very challenging, the Kohlberg criterion offers a powerful method for verifying whether a solution is the nucleolus in relatively small games (i.e. with the number of players n≤ 15). This approach, however, becomes more challenging for larger games because of the need to form and check a criterion involving possibly exponentially large collections of coalitions, with each collection potentially of an exponentially large size. The aim of this work is twofold. First, we develop an improved version of the Kohlberg criterion that involves checking the ‘balancedness’ of at most (n- 1) sets of coalitions. Second, we exploit these results and introduce a novel descent-based constructive algorithm to find the nucleolus efficiently. We demonstrate the performance of the new algorithms by comparing them with existing methods over different types of games. Our contribution also includes the first open-source code for computing the nucleolus for games of moderately large sizes. © 2020, The Author(s)

A distributionally robust optimization approach for two-stage facility location problems

In this paper, we consider a facility location problem where customer demand constitutes considerable uncertainty, and where complete information on the distribution of the uncertainty is unavailable. We formulate the optimal decision problem as a two-stage stochastic mixed integer programming problem: an optimal selection of facility locations in the first stage and an optimal decision on the operation of each facility in the second stage. A distributionally robust optimization framework is proposed to hedge risks arising from incomplete information on the distribution of the uncertainty. Specifically, by exploiting the moment information, we construct a set of distributions which contains the true distribution and where the optimal decision is based on the worst distribution from the set. We then develop two numerical schemes for solving the distributionally robust facility location problem: a semi-infinite programming approach which exploits moments of certain reference random variables and a semi-definite programming approach which utilizes the mean and correlation of the underlying random variables describing the demand uncertainty. In the semi-infinite programming approach, we apply the well-known linear decision rule approach to the robust dual problem and then approximate the semi-infinite constraints through the conditional value at risk measure. We provide numerical tests to demonstrate the computation and properties of the robust solutions. © 2020, The Author(s)