Commande optimale stochastique appliquée aux systèmes manufacturiers avec des sauts semi-Markoviens

Les travaux de ce mémoire sont constitués de deux parties principales. La première partie tente de formuler un nouveau modèle du problème de commande optimale stochastique de systèmes sur un horizon fini. Les systèmes considérés sont soumis à des phénomènes aléatoires dits sauts de perturbation qui sont modélisés par un processus semi-Markovien. Ces sauts de perturbation traduits par des taux de transition dépendent de l’état du système et du temps. Par conséquent, le problème de commande est formulé comme un problème d’optimisation dans un environnement stochastique. La deuxième partie vise à modéliser des systèmes de production flexible (SPF). Dans ce mémoire, ces SPF se composent de plusieurs machines en parallèles, ou en série, ou d’une station de travail (une machine représentative). Ces machines sont sujettes à des pannes et à des réparations aléatoires. L’objectif de la modélisation est de déterminer les taux de production u(t) de ces machines en satisfaisant les fluctuations de demande d(t) sur un horizon fini. Dans ce mémoire, nous avons : (a) proposé un nouveau modèle du problème d’optimisation dans un environnement stochastique sur un horizon fini pour deux cas; avec taux d’actualisation (ρ > 0) et sans taux d’actualisation (ρ = 0); (b) modélisé des SPF en déterminant une stratégie de commande plus réaliste incluant stratégie de production; (c) présenté des exemples numériques à l’aide d’une méthode de Kushner et Dupuis (2001)

Stochastic control for optimal power flow in islanded microgrid

The problem of optimal power flow (OPF) in an islanded mircrogrid (MG) for hybrid power system is described. Clearly, it deals with a formulation of an analytical control model for OPF. The MG consists of wind turbine generator, photovoltaic generator, and diesel engine generator (DEG), and is in stochastic environment such as load change, wind power fluctuation, and sun irradiation power disturbance. In fact, the DEG fails and is repaired at random times so that the MG can significantly influence the power flow, and the power flow control faces the main difficulty that how to maintain the balance of power flow? The solution is that a DEG needs to be scheduled. The objective of the control problem is to find the DEG output power by minimizing the total cost of energy. Adopting the Rishel’s famework and using the Bellman principle, the optimality conditions obtained satisfy the Hamilton-Jacobi-Bellman equation. Finally, numerical examples and sensitivity analyses are included to illustrate the importance and effectiveness of the proposed model

Network-Aided Intelligent Traffic Steering in 6G O-RAN: A Multi-Layer Optimization Framework

To enable an intelligent, programmable and multi-vendor radio access network (RAN) for 6G networks, considerable efforts have been made in standardization and development of open RAN (O-RAN). So far, however, the applicability of O-RAN in controlling and optimizing RAN functions has not been widely investigated. In this paper, we jointly optimize the flow-split distribution, congestion control and scheduling (JFCS) to enable an intelligent traffic steering application in O-RAN. Combining tools from network utility maximization and stochastic optimization, we introduce a multi-layer optimization framework that provides fast convergence, long-term utility-optimality and significant delay reduction compared to the state-of-the-art and baseline RAN approaches. Our main contributions are three-fold: i) we propose the novel JFCS framework to efficiently and adaptively direct traffic to appropriate radio units; ii) we develop low-complexity algorithms based on the reinforcement learning, inner approximation and bisection search methods to effectively solve the JFCS problem in different time scales; and iii) the rigorous theoretical performance results are analyzed to show that there exists a scaling factor to improve the tradeoff between delay and utility-optimization. Collectively, the insights in this work will open the door towards fully automated networks with enhanced control and flexibility. Numerical results are provided to demonstrate the effectiveness of the proposed algorithms in terms of the convergence rate, long-term utility-optimality and delay reduction.Comment: 15 pages, 10 figures. A short version will be submitted to IEEE GLOBECOM 202

Network-Aided Intelligent Traffic Steering in 6G O-RAN: A Multi-Layer Optimization Framework

peer reviewedTo enable an intelligent, programmable and multi-vendor radio access network (RAN) for 6G networks, considerable efforts have been made in standardization and development of open RAN (O-RAN). So far, however, the applicability of O-RAN in controlling and optimizing RAN functions has not been widely investigated. In this paper, we jointly optimize the flow-split distribution, congestion control and scheduling (JFCS) to enable an intelligent traffic steering application in O-RAN. Combining tools from network utility maximization and stochastic optimization, we introduce a multi-layer optimization framework that provides fast convergence, long-term utility-optimality and significant delay reduction compared to the state-of-the-art and baseline RAN approaches. Our main contributions are three-fold: i ) we propose the novel JFCS framework to efficiently and adaptively direct traffic to appropriate radio units; ii ) we develop low-complexity algorithms based on the reinforcement learning, inner approximation and bisection search methods to effectively solve the JFCS problem in different time scales; and iii ) the rigorous theoretical performance results are analyzed to show that there exists a scaling factor to improve the tradeoff between delay and utility-optimization. Collectively, the insights in this work will open the door towards fully automated networks with enhanced control and flexibility. Numerical results are provided to demonstrate the effectiveness of the proposed algorithms in terms of the convergence rate, long-term utility-optimality and delay reduction

Power flow analysis for islanded microgrid in hierarchical structure of control system using optimal control theory

In environmental uncertainties, the power flow problem in islanded microgrid (MG) becomes complex and non-trivial. The optimal power flow (OPL) problem is described in this paper by using the energy balance between the power generation and load demand. The paper also presents the hierarchical control structure which consists of primary, secondary, tertiary, and emergency controls. Clearly, optimal power flow (OPL) which implements a distributed tertiary control in hierarchical control. MG consists of diesel engine generator (DEG), wind turbine generator (WTG), and photovoltaic (PV) power. In the control system considered, operation planning is realized based on profiles such that the MG, load, wind and photovoltaic power must be forecasted in short-period, meanwhile the dispatch source (i.e., DEG) needs to be scheduled. The aim of the control problem is to find the dispatch output power by minimizing the total cost of energy that leads to the Hamilton-Jacobi-Bellman equation. Experimental results are presented, showing the effectiveness of optimal control such that the generation allows demand profile

Pharmacist-Led Interventions to Reduce Drug-Related Problems in Prescribing for Pediatric Outpatients in a Developing Country:A Randomized Controlled Trial

OBJECTIVE To evaluate a pharmacist-led intervention’s effectiveness in reducing drug-related problems (DRPs ) related to prescriptions for pediatric outpatients. METHODS We conducted a randomized controlled trial. We recruited and randomly assigned 31 physicians to control or intervention groups. We collected 775 prescriptions (375 from the control group and 400 from the intervention group) at the start. For 3 weeks, intervention physicians received additional information and meetings with pharmacists in addition to the usual practices of the hospital. We then collected prescriptions at the end of the study. We classified DRPs, based on reliable references (Supplemental Table S1) at baseline and endpoint (a week after the intervention). The primary outcome was the proportion of prescriptions with DRPs, and secondary outcomes were the proportions of prescriptions with specific DRP types. RESULTS The influence of the intervention on general DRPs and specific DRPs was the study’s main finding. The pharmacist-led intervention helped reduce the prescriptions with DRPs proportion in the intervention group to 41.0%, compared with 49.3% in the control group (p &lt; 0.05). The DRPs proportion related to the timing of administration relative to meals, unlike the other DRP types, increased in the control group (from 31.7% to 34.9%) and decreased in the intervention group (from 31.3% to 25.3%), with a significant difference between the 2 groups at endpoint (p &lt; 0.01). Patients aged &gt;2 to ≤6 years (OR, 1.871; 95% CI, 1.340–2.613) and receiving ≥5 drugs (OR, 5.037; 95% CI, 2.472–10.261) were at greater risk of experiencing DRPs related to prescribing. CONCLUSIONS A pharmacist-led intervention improved DRP occurrence related to physicians’ prescribing. Pharmacists could be involved in in-depth research with physicians in the prescribing process to provide tailored interventions.</p

Medication Adherence and Belief about Medication among Vietnamese Patients with Chronic Cardiovascular Diseases Within the Context of Implementing Measures to Prevent COVID-19

Background: Long-term adherence is crucial for optimal treatment outcomes in chronic cardiovascular diseases (CVDs), especially throughout the COVID-19 wide-spreading periods, making patients with chronic CVDs vulnerable subjects. Aim: To investigate the relationship between the characteristics, beliefs about prescribed medication, COVID-19 prevention measures, and medication adherence among patients with chronic CVDs. Methods: This is a cross-sectional study of outpatients with chronic CVDs in Southern Vietnam. The specific parts regarding the Beliefs about Medicines Questionnaires (BMQ-Specific) and the General Medication Adherence Scale (GMAS) were applied to assess the beliefs about and adherence to medication. The implementation measures to prevent COVID-19 in patients were evaluated according to the 5K message (facemask, disinfection, distance, no gathering, and health declaration) of the Vietnam Ministry of Health. A multivariable logistic regression with the Backward elimination (Wald) method was used to identify the associated factors of medication adherence. Results: A slightly higher score in BMQ-Necessity compared to BMQ-Concerns was observed. A total of 40.7% of patients were recorded as having not adhered to their medications. Patients' behavior was most frequently self-reported by explaining their non-adherence (34.7%). Statistical associations were found between rural living place, unemployment status, no or only one measure(s) of COVID-19 prevention application, and medication adherence. Conclusion: During the COVID-19 spreading stage, patients generally showed a positive belief about medication when they rated the importance of taking it higher than its side effects. The data analysis suggested that rather than patients’ beliefs, the clinicians should consider the patient factors, including living place, employment, and the number of epidemic preventive measures applied for guiding the target patients for improving medication adherence

Age distribution of dengue cases in southern Vietnam from 2000 to 2015.

BackgroundDengue is the most common vector-borne viral infection. In recent times, an increase in the age of cases with clinical dengue has been reported in the national surveillance system and published literature of Vietnam. This change not only alter the risk of transmission and disease burden in different populations but also will impact for prevention and control strategies. A retrospective study was conducted from 2000 to 2015 in 19 provinces of southern Vietnam to describe the changes in age distribution of dengue cases and circulating serotypes.Methodology/principal findingsThe study is a time trend analysis of the data aggregated from the database of dengue surveillance system. The database consisted of clinically diagnosed and laboratory-confirmed cases of dengue in southern Vietnam from 2000 to 2015. In the study period, the mean age of dengue cases increased from 12.2 ± 8.8 years old (y/o) to 16.8 ± 13.3 y/o between 2000 and 2015. Majority of severe cases were observed in the age group of 5-9 y/o and 10-14 y/o. Overall, the mortality and case fatality rates (CFR) were lowest during 2010 to 2015, and all four serotypes of dengue were observed.Conclusions/significanceWith the exception of severe form, the age distribution of clinical cases of dengue appears to be shifting towards older age groups. An increase in the mean age of clinical cases of dengue has been observed in southern Vietnam over the past decade, and the highest incidence was observed in age group of 5-14 y/o. All serotypes of dengue were in circulation

Medication Adherence in Vietnamese Patients with Cardiovascular and Endocrine&ndash;Metabolic Diseases

(1) Background: COVID-19 has significantly affected the quality of life and the medication adherence of patients with chronic diseases. Attitudes towards the disease and preventive measures are the things that need to be considered for patient adherence to medication during the COVID-19 pandemic. We aimed to evaluate the rate and compare the medication adherence and the impact of the COVID-19 pandemic on medication adherence in Vietnamese patients with cardiovascular and endocrine&ndash;metabolic diseases. (2) Methods: A cross-sectional study was conducted on outpatients having chronic diseases such as cardiovascular or/and endocrine&ndash;metabolic diseases in some southern provinces in Vietnam. In each group of patients, medication adherence was measured and assessed with the General Medication Adherence Scale (GMAS), adjusted and validated in Vietnam. In addition, the study also investigated attitudes and practices to prevent COVID-19. (3) Results: Out of 1444 patients in our study, the level of adherence was recorded in 867 cases, accounting for 61.1%. The group of patients with only cardiovascular disease and patients with only endocrine&ndash;metabolic disease had relatively similar compliance rates of 62 and 61.1%, respectively. The leading cause of non-adherence to treatment in all three groups of patients in the study, as assessed by the GMAS, was non-adherence due to financial constraints. Our study showed that 71.6% of patients felt anxious when going to the hospital for a medical examination. However, only 53.7% identified the COVID-19 pandemic as obstructing treatment follow-up visits. The research results showed that the COVID-19 epidemic influences the patient&rsquo;s psychology with regard to re-examination and treatment adherence, with p coefficients of 0.003 and &lt;0.001, respectively. (4) Conclusion: Medication adherence rates in two disease groups are close, and financial constraint is the fundamental reason for medication non-adherence. Regulatory agencies must take care of people&rsquo;s welfare to improve adherence in the epidemic context

Medication Adherence of Vietnamese Outpatients with Chronic Diseases during the COVID-19 Pandemic

The purpose of this study was to determine the medication adherence of outpatients with chronic diseases and the association between both patient attitudes and preventive practices regarding COVID-19 and their medication adherence. We performed a cross-sectional study in Vietnam. Medication adherence was determined using the translated and validated Vietnamese version of the General Medication Adherence Scale (GMAS). Patient attitudes and preventive practices regarding COVID-19 were measured using the 5K message of the Vietnam Ministry of Health (facemasks, disinfection, distance, no gatherings, health declarations). The associations between patient characteristics and medication adherence were determined by multivariable regression. The study included 1852 outpatients, and 57.6% of the patients adhered to their medications. Patients who recognized the pandemic&rsquo;s obstruction of medical follow-ups (OR = 1.771; 95%CI = 1.461&ndash;2.147; p &lt; 0.001), who applied &ge;2 preventive methods (OR = 1.422; 95%CI = 1.173&ndash;1.725; p = 0.001), who were employed (OR = 1.677; 95%CI = 1.251&ndash;2.248; p = 0.001), who were living in urban areas (OR = 1.336; 95%CI = 1.090&ndash;1.637; p = 0.005,) who possessed higher education levels (OR = 1.313; 95%CI = 1.059&ndash;1.629; p = 0.013), or who had &le;2 comorbidities (OR = 1.293; 95%CI = 1.044&ndash;1.600; p = 0.019) were more likely to adhere to their medications. The adherence percentage for outpatients with chronic diseases was quite low during the pandemic. Patients who did not recognize the COVID-19 pandemic&rsquo;s obstruction of medical follow-ups or who had poor preventive practices were less likely to adhere to medications. Healthcare providers should pay more attention to these groups to achieve desired treatment outcomes