Convergence of trajectories and optimal buffer sizing for AIMD congestion control

We study the interaction between the AIMD (Additive Increase Multiplicative Decrease) multi-socket congestion control and a bottleneck router with Drop Tail buffer. We consider the problem in the framework of deterministic hybrid models. First, we show that trajectories always converge to limiting cycles. We characterize the cycles. Necessary and sufficient conditions for the absence of multiple jumps in the same cycle are obtained. Then, we propose an analytical framework for the optimal choice of the router buffer size. We formulate this problem as a multi-criteria optimization problem, in which the Lagrange function corresponds to a linear combination of the average goodput and the average delay in the queue. Our analytical results are confirmed by simulations performed with MATLAB Simulink

Convergence of trajectories and optimal buffer sizing for MIMD congestion control

We study the interaction between the MIMD (Multiplicative Increase Multiplicative Decrease) congestion control and a bottleneck router with Drop Tail buffer. We consider the problem in the framework of deterministic hybrid models. We study conditions under which the system trajectories converge to limiting cycles with a single jump. Following that, we consider the problem of the optimal buffer sizing in the framework of multi-criteria optimization in which the Lagrange function corresponds to a linear combination of the average throughput and the average delay in the queue. As case studies, we consider the Slow Start phase of TCP New Reno and Scalable TCP for high speed networks. © 2009 Elsevier B.V. All rights reserved

Learning the progression patterns of treatments using a probabilistic generative model

Modeling a disease or the treatment of a patient has drawn much attention in recent years due to the vast amount of information that Electronic Health Records contain. This paper presents a probabilistic generative model of treatments that are described in terms of sequences of medical activities of variable length. The main objective is to identify distinct subtypes of treatments for a given disease, and discover their development and progression. To this end, the model considers that a sequence of actions has an associated hierarchical structure of latent variables that both classifies the sequences based on their evolution over time, and segments the sequences into different progression stages. The learning procedure of the model is performed with the Expectation–Maximization algorithm which considers the exponential number of configurations of the latent variables and is efficiently solved with a method based on dynamic programming. The evaluation of the model is twofold: first, we use synthetic data to demonstrate that the learning procedure allows the generative model underlying the data to be recovered; we then further assess the potential of our model to provide treatment classification and staging information in real-world data. Our model can be seen as a tool for classification, simulation, data augmentation and missing data imputation.EJ-GV PREDOC 201

Identifying common treatments from Electronic Health Records with missing information. An application to breast cancer.

The aim of this paper is to analyze the sequence of actions in the health system associated with a particular disease. In order to do that, using Electronic Health Records, we define a general methodology that allows us to: (I) identify the actions in the health system associated with a disease; (ii) identify those patients with a complete treatment for the disease; (iii) and discover common treatment pathways followed by the patients with a specific diagnosis. The methodology takes into account the characteristics of the EHRs, such as record heterogeneity and missing information. As an example, we use the proposed methodology to analyze breast cancer disease. For this diagnosis, 5 groups of treatments, which fit in with medical practice guidelines and expert knowledge, were obtained.Artificial Intelligence in BCAM number EXP. 2019/00432, PID2019-104966GB-I00, TIN2016-78365-R, IT1244-19

Blisk blades manufacturing technologies analysis

The paper presents blisk blades manufactured by different manufacturing processes. In this sense, different milling trajectories are presented, and, super abrasive machining strategies and EDM technologies are also tested. Machining times, costs and surface finish are analysed in order to determine optimal machining process for blisk manufactured in low machinability materials.RYC-2017-2264

A Machine Learning Approach to Predict Healthcare Cost of Breast Cancer Patients

This paper presents a novel machine learning approach to per- form an early prediction of the healthcare cost of breast cancer patients. The learning phase of our prediction method considers the following two steps: i) in the first step, the patients are clustered taking into account the sequences of ac- tions undergoing similar clinical activities and ensuring similar healthcare costs, and ii) a Markov chain is then learned for each group to describe the action- sequences of the patients in the cluster. A two step procedure is undertaken in the prediction phase: i) first, the healthcare cost of a new patient’s treatment is estimated based on the average healthcare cost of its k−nearest neighbors in each group, and ii) finally, an aggregate measure of the healthcare cost estimated by each group is used as the final predicted cost. Experiments undertaken reveal a mean absolute percentage error as small as 6%, even when half of the clinical records of a patient is available, substantiating the early prediction capability of the proposed method. Comparative analysis substantiates the superiority of the proposed algorithm over the state-of-the-art techniques.IT1244-19 PID2019-104966GB-I00 TIN2016-78365-

Impact of the ENSP eLearning platform on improving knowledge, attitudes and self-efficacy for treating tobacco dependence: An assessment across 15 European countries

Introduction: In 2018, the European Network for Smoking Cessation and Prevention (ENSP) released an update to its Tobacco Treatment Guidelines for healthcare professionals, which was the scientific base for the development of an accredited eLearning curriculum to train healthcare professionals, available in 14 languages. The aim of this study was to evaluate the effectiveness of ENSP eLearning curriculum in increasing healthcare professionals' knowledge, attitudes, self-efficacy (perceived behavioral control) and intentions in delivering tobacco treatment interventions in their daily clinical routines. Methods: We conducted a quasi-experimental pre-post design study with 444 healthcare professionals, invited by 20 collaborating institutions from 15 countries (Albania, Armenia, Belgium, Italy, France, Georgia, Greece, Kosovo, Romania, North Macedonia, Russia, Serbia, Slovenia, Spain, Ukraine), which completed the eLearning course between December 2018 and July 2019. Results: Healthcare professionals' self-reported knowledge improved after the completion of each module of the eLearning program. Increases in healthcare professionals' self-efficacy in delivering tobacco treatment interventions (p<0.001) were also documented. Significant improvements were documented in intentions to address tobacco use as a priority, document tobacco use, offer support, provide brief counselling, give written material, discuss available medication, prescribe medication, schedule dedicated appointment to develop a quit plan, and be persistent in addressing tobacco use with the patients (all p<0.001). Conclusions: An evidence-based digital intervention can be effective in improving knowledge, attitudes, self-efficacy and intentions on future delivery of tobacco-treatment interventions

Current practices and perceived barriers to tobacco-treatment delivery among healthcare professionals from 15 European countries. The EPACTT Plus project

Introduction: The latest evidence-based Guidelines for Treating Tobacco Dependence highlight the significant role of healthcare professionals in supporting smokers interested to quit. This study aimed to identify the current practices of healthcare professionals in Europe and perceived barriers in delivering tobacco treatment to their patients who smoke. Methods: In the context of EPACTT-Plus, collaborating institutions from 15 countries (Albania, Armenia, Belgium, Italy, France, Georgia, Greece, Kosovo, Romania, North Macedonia, Russia, Serbia, Slovenia, Spain, Ukraine) worked for the development of an accredited eLearning course on Tobacco Treatment Delivery available at http://elearning-ensp.eu/. In total, 444 healthcare professionals from the wider European region successfully completed the course from December 2018 to July 2019. Cross-sectional data were collected online on healthcare professionals' current practices and perceived barriers in introducing tobacco-dependence treatment into their daily clinical life. Results: At registration, 41.2% of the participants reported having asked their patients if they smoked. Advise to quit smoking was offered by 47.1% of the participants, while 29.5% reported offering assistance to their patients who smoked in order to quit. From the total number of participants, 39.9% regarded the lack of patient compliance as a significant barrier. Other key barriers were lack of: interest from the patients (37.4%), healthcare professionals training (33.1%), community resources to refer patients (31.5%), and adequate time during their everyday clinical life (29.7%). Conclusions: The identification of current practices and significant barriers is important to build evidence-based guidelines and training programs (online and/or live) that will improve the performance of healthcare professionals in offering tobacco-dependence treatment for their patients who smoke