Characterization of early graft damage after pancreatic transplantation

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Robust Minimum Cost Flow Problem Under Consistent Flow Constraints

The robust minimum cost flow problem under consistent flow constraints (RobMCF$\equiv$) is a new extension of the minimum cost flow (MCF) problem. In the RobMCF$\equiv$ problem, we consider demand and supply that are subject to uncertainty. For all demand realizations, however, we require that the flow value on an arc needs to be equal if it is included in the predetermined arc set given. The objective is to find feasible flows that satisfy the equal flow requirements while minimizing the maximum occurring cost among all demand realizations. In the case of a discrete set of scenarios, we derive structural results which point out the differences with the polynomial time solvable MCF problem on networks with integral capacities. In particular, the Integral Flow Theorem of Dantzig and Fulkerson does not hold. For this reason, we require integral flows in the entire paper. We show that the RobMCF$\equiv$ problem is strongly $\mathcal{NP}$-hard on acyclic digraphs by a reduction from the $(3,B2)$-Sat problem. Further, we demonstrate that the RobMCF$\equiv$ problem is weakly $\mathcal{NP}$-hard on series-parallel digraphs by providing a reduction from Partition and a pseudo-polynomial algorithm based on dynamic programming. Finally, we propose a special case on series-parallel digraphs for which we can solve the RobMCF$\equiv$ problem in polynomial time

Анализ проблем инновационного развития медицины в Украине

Проанализированы проблемы, тормозящие развитие инновационной деятельности в медицине Украины, и внесены предложения по их устранению.Проаналізовано проблеми, які стримують розвиток інноваційної діяльності в медицині України, і внесено пропозиції щодо їх усунення.The paper contains an analysis of barriers for innovation in the Ukrainian medical sector, with propositions for their elimination

An (MI)LP-based Primal Heuristic for 3-Architecture Connected Facility Location in Urban Access Network Design

We investigate the 3-architecture Connected Facility Location Problem arising in the design of urban telecommunication access networks. We propose an original optimization model for the problem that includes additional variables and constraints to take into account wireless signal coverage. Since the problem can prove challenging even for modern state-of-the art optimization solvers, we propose to solve it by an original primal heuristic which combines a probabilistic fixing procedure, guided by peculiar Linear Programming relaxations, with an exact MIP heuristic, based on a very large neighborhood search. Computational experiments on a set of realistic instances show that our heuristic can find solutions associated with much lower optimality gaps than a state-of-the-art solver.Comment: This is the authors' final version of the paper published in: Squillero G., Burelli P. (eds), EvoApplications 2016: Applications of Evolutionary Computation, LNCS 9597, pp. 283-298, 2016. DOI: 10.1007/978-3-319-31204-0_19. The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-31204-0_1

Recoverable Robust Knapsacks: the Discrete Scenario Case

Admission control problems have been studied extensively in the past. In a typical setting, resources like bandwidth have to be distributed to the different customers according to their demands maximizing the profit of the company. Yet, in real-world applications those demands are deviating and in order to satisfy their service requirements often a robust approach is chosen wasting benefits for the company. Our model overcomes this problem by allowing a limited recovery of a previously fixed assignment as soon as the data are known by violating at most k service promises and serving up to l new customers. Applying this approaches to the call admission problem on a single link of a telecommunication network leads to a recoverable robust version of the knapsack problem

Evaluation of the Efficacy of Single Anastomosis Sleeve Ileal (SASI) Bypass for Patients with Morbid Obesity: a Multicenter Study

Background: Single anastomosis sleeve ileal (SASI) bypass is a newly introduced bariatric and metabolic procedure. The present multicenter study aimed to evaluate the efficacy of the SASI bypass in the treatment of patients with morbid obesity and the metabolic syndrome. Methods: This is a retrospective, seven-country, multicenter study on patients with morbid obesity who underwent the SASI bypass. Data regarding patients' demographics, body mass index (BMI), percentage of total weight loss (%TWL), percentage of excess weight loss (%EWL), and improvement in comorbidities at 12 months postoperatively and postoperative complications were collected. Results: Among 605 patients who underwent the SASI, 54 were excluded and 551 (390; 70.8% female) were included. At 12 months after the SASI, a significant decrease in the BMI was observed (43.2 ± 12.5 to 31.2 ± 9.7 kg/m2; p < 0.0001). The %TWL was 27.4 ± 13.4 and the %EWL was 63.9 ± 29.5. Among the 279 patients with type 2 diabetes mellitus (T2DM), complete remission was recorded in 234 (83.9%) patients and partial improvement in 43 (15.4%) patients. Eighty-six (36.1%) patients with hypertension, 104 (65%) patients with hyperlipidemia, 37 (57.8%) patients with sleep apnea, and 70 (92.1%) patients with GERD achieved remission. Fifty-six (10.1%) complications and 2 (0.3%) mortalities were recorded. Most complications were minor. All patients had 12 months follow-up. Conclusions: The SASI bypass is an effective bariatric and metabolic surgery that achieved satisfactory weight loss and improvement in medical comorbidities, including T2DM, hypertension, sleep apnea, and GERD, with a low complication rate.info:eu-repo/semantics/publishedVersio

A fast ILP-based Heuristic for the robust design of Body Wireless Sensor Networks

We consider the problem of optimally designing a body wireless sensor network, while taking into account the uncertainty of data generation of biosensors. Since the related min-max robustness Integer Linear Programming (ILP) problem can be difficult to solve even for state-of-the-art commercial optimization solvers, we propose an original heuristic for its solution. The heuristic combines deterministic and probabilistic variable fixing strategies, guided by the information coming from strengthened linear relaxations of the ILP robust model, and includes a very large neighborhood search for reparation and improvement of generated solutions, formulated as an ILP problem solved exactly. Computational tests on realistic instances show that our heuristic finds solutions of much higher quality than a state-of-the-art solver and than an effective benchmark heuristic.Comment: This is the authors' final version of the paper published in G. Squillero and K. Sim (Eds.): EvoApplications 2017, Part I, LNCS 10199, pp. 1-17, 2017. DOI: 10.1007/978-3-319-55849-3\_16. The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-55849-3_1