Chance-Constrained Control with Lexicographic Deep Reinforcement Learning

This paper proposes a lexicographic Deep Reinforcement Learning (DeepRL)-based approach to chance-constrained Markov Decision Processes, in which the controller seeks to ensure that the probability of satisfying the constraint is above a given threshold. Standard DeepRL approaches require i) the constraints to be included as additional weighted terms in the cost function, in a multi-objective fashion, and ii) the tuning of the introduced weights during the training phase of the Deep Neural Network (DNN) according to the probability thresholds. The proposed approach, instead, requires to separately train one constraint-free DNN and one DNN associated to each constraint and then, at each time-step, to select which DNN to use depending on the system observed state. The presented solution does not require any hyper-parameter tuning besides the standard DNN ones, even if the probability thresholds changes. A lexicographic version of the well-known DeepRL algorithm DQN is also proposed and validated via simulations

Bellman's principle of optimality and deep reinforcement learning for time-varying tasks

This paper presents the first framework (up to the authors' knowledge) to address time-varying objectives in finite-horizon Deep Reinforcement Learning (DeepRL), based on a switching control solution developed on the ground of Bellman's principle of optimality. By augmenting the state space of the system with information on its visit time, the DeepRL agent is able to solve problems in which its task dynamically changes within the same episode. To address the scalability problems caused by the state space augmentation, we propose a procedure to partition the episode length to define separate sub-problems that are then solved by specialised DeepRL agents. Contrary to standard solutions, with the proposed approach the DeepRL agents correctly estimate the value function at each time-step and are hence able to solve time-varying tasks. Numerical simulations validate the approach in a classic RL environment

Controlled optimal black start procedures in smart grids for service restoration in presence of electrical storage systems

This paper presents an optimisation problem to determine the optimal reclosure order of remotely operable switches deployed in a smart grid consisting in a distribution network equipped with one or more Energy Storage Systems (ESS). The proposed solution integrates nonlinear real and reactive power flow equations, by reconducting them to a set of conic constraints, together with several network operator requirements, such as network radiality and ampacity limits. A numerical simulation validates the approach and concludes the work

Joint Model Predictive Control of Electric and Heating Resources in a Smart Building

The new challenge in power systems design and operation is to organize and control smart micro grids supplying aggregation of users and special loads as electric vehicles charging stations. The presence of renewable and storage can help the optimal operation only if a good control manages all the elements of the grid. New models of green buildings and energy communities are proposed. For a real application they need an appropriate and advanced power system equipped with a building automation control system. This article presents an economic model predictive control approach to the problem of managing the electric and heating resources in a smart building in a coordinated way, for the purpose of achieving in real time nearly zero energy consumption and automated participation to demand response programs. The proposed control, leveraging a mixed integer quadratic programming problem, allows to meet manifold thermal and electric users' requirements and react to inbound demand response signals, while still guaranteeing stable operation of the building's electric and thermal storage equipment. The simulation results, performed for a real case study in Italy, highlight the peculiarities of the proposed approach in the joint handling of electric and thermal building flexibility

Sotatercept safety and effects on hemoglobin, bone, and vascular calcification

Introduction: Patients with end-stage kidney disease (ESKD) exhibit anemia, chronic kidney disease‒mineral bone disorder (CKD-MBD), and cardiovascular disease. The REN-001 and REN-002 phase II, multicenter, randomized studies examined safety, tolerability, and effects of sotatercept, an ActRIIA-IgG1 fusion protein trap, on hemoglobin concentration; REN-001 also explored effects on bone mineral density (BMD) and abdominal aortic vascular calcification. Methods: Forty-three patients were treated in REN-001 (dose range: sotatercept 0.3‒0.7 mg/kg or placebo subcutaneously [s.c.] for 200 days) and 50 in REN-002 (dose range: 0.1‒0.4 mg/kg i.v. and 0.13‒0.5 mg/kg s.c. for 99 days). Results: In REN-001, frequency of achieving target hemoglobin response (\u3e10 g/dl [6.21 mmol/l]) with sotatercept was dose-related and greater than placebo (0.3 mg/kg: 33.3%; 0.5 mg/kg: 62.5%; 0.7 mg/kg: 77.8%; 0.7 mg/kg [doses 1 and 2]/0.4 mg/kg [doses 3‒15]: 33.3%; placebo: 27.3%). REN-002 hemoglobin findings were similar (i.v.: 16.7%-57.1%; s.c.: 11.1%‒42.9%). Dose-related achievement of ≥2% increase in femoral neck cortical BMD was seen among only REN-001 patients receiving sotatercept (0.3‒0.7 mg/kg: 20.0%‒57.1%; placebo: 0.0%). Abdominal aortic vascular calcification was slowed in a dose-related manner, with a ≤15% increase in Agatston score achieved by more REN-001 sotatercept versus placebo patients (60%‒100% vs. 16.7%). The most common adverse events during treatment were hypertension, muscle spasm, headache, arteriovenous fistula site complication, and influenza observed in both treatment and placebo groups. Conclusion: In patients with ESKD, sotatercept exhibited a favorable safety profile and was associated with trends in dose-related slowing of vascular calcification. Less-consistent trends in improved hemoglobin concentration and BMD were observed

Design of cellular, satellite, and integrated systems for 5G and beyond

5G AgiLe and fLexible integration of SaTellite And cellulaR (5G-ALLSTAR) is a Korea-Europe (KR-EU) collaborative project for developing multi-connectivity (MC) technologies that integrate cellular and satellite networks to provide seamless, reliable, and ubiquitous broadband communication services and improve service continuity for 5G and beyond. The main scope of this project entails the prototype development of a millimeter-wave 5G New Radio (NR)-based cellular system, an investigation of the feasibility of an NR-based satellite system and its integration with cellular systems, and a study of spectrum sharing and interference management techniques for MC. This article reviews recent research activities and presents preliminary results and a plan for the proof of concept (PoC) of three representative use cases (UCs) and one joint KR-EU UC. The feasibility of each UC and superiority of the developed technologies will be validated with key performance indicators using corresponding PoC platforms. The final achievements of the project are expected to eventually contribute to the technical evolution of 5G, which will pave the road for next-generation communications

ACCF/AHA 2011 Expert Consensus Document on Hypertension in the Elderly: A Report of the American College of Cardiology Foundation Task Force on Clinical Expert Consensus Documents

This document was written with the intent to be a complete reference at the time of publication on the topic of managing hypertension in the elderly. This document has been developed as an expert consensus document by the American College of Cardiology Foundation (ACCF) and the American Heart Association (AHA), in collaboration with the American Academy of Neurology (AAN), the American College of Physicians (ACP), the American Geriatrics Society (AGS), the American Society of Hypertension (ASH), the American Society of Nephrology (ASN), the American Society for Preventive Cardiology (ASPC), the Association of Black Cardiologists (ABC), and the European Society of Hypertension (ESH). Expert consensus documents are intended to inform practitioners, payers, and other interested parties of the opinion of ACCF and document cosponsors concerning evolving areas of clinical practice and/or technologies that are widely available or new to the practice community

ACCF/AHA 2011 Expert Consensus Document on Hypertension in the Elderly: A Report of the American College of Cardiology Foundation Task Force on Clinical Expert Consensus Documents

This document was written with the intent to be a complete reference at the time of publication on the topic of managing hypertension in the elderly. This document has been developed as an expert consensus document by the American College of Cardiology Foundation (ACCF) and the American Heart Association (AHA), in collaboration with the American Academy of Neurology (AAN), the American College of Physicians (ACP), the American Geriatrics Society (AGS), the American Society of Hypertension (ASH), the American Society of Nephrology (ASN), the American Society for Preventive Cardiology (ASPC), the Association of Black Cardiologists (ABC), and the European Society of Hypertension (ESH). Expert consensus documents are intended to inform practitioners, payers, and other interested parties of the opinion of ACCF and document cosponsors concerning evolving areas of clinical practice and/or technologies that are widely available or new to the practice community

RETRATO DE MUJER [Material gráfico]

Copia digital. Madrid : Ministerio de Educación, Cultura y Deporte, 201

Deep reinforcement learning control of white-light continuum generation

White-light continuum (WLC) generation in bulk media finds numerous applications in ultrafast optics and spectroscopy. Due to the complexity of the underlying spatiotemporal dynamics, WLC optimization typically follows empirical procedures. Deep reinforcement learning (RL) is a branch of machine learning dealing with the control of automated systems using deep neural networks. In this Letter, we demonstrate the capability of a deep RL agent to generate a long-term-stable WLC from a bulk medium without any previous knowledge of the system dynamics or functioning. This work demonstrates that RL can be exploited effectively to control complex nonlinear optical experiments