From Uncertainty Data to Robust Policies for Temporal Logic Planning

We consider the problem of synthesizing robust disturbance feedback policies for systems performing complex tasks. We formulate the tasks as linear temporal logic specifications and encode them into an optimization framework via mixed-integer constraints. Both the system dynamics and the specifications are known but affected by uncertainty. The distribution of the uncertainty is unknown, however realizations can be obtained. We introduce a data-driven approach where the constraints are fulfilled for a set of realizations and provide probabilistic generalization guarantees as a function of the number of considered realizations. We use separate chance constraints for the satisfaction of the specification and operational constraints. This allows us to quantify their violation probabilities independently. We compute disturbance feedback policies as solutions of mixed-integer linear or quadratic optimization problems. By using feedback we can exploit information of past realizations and provide feasibility for a wider range of situations compared to static input sequences. We demonstrate the proposed method on two robust motion-planning case studies for autonomous driving

Algorithm Engineering in Robust Optimization

Robust optimization is a young and emerging field of research having received a considerable increase of interest over the last decade. In this paper, we argue that the the algorithm engineering methodology fits very well to the field of robust optimization and yields a rewarding new perspective on both the current state of research and open research directions. To this end we go through the algorithm engineering cycle of design and analysis of concepts, development and implementation of algorithms, and theoretical and experimental evaluation. We show that many ideas of algorithm engineering have already been applied in publications on robust optimization. Most work on robust optimization is devoted to analysis of the concepts and the development of algorithms, some papers deal with the evaluation of a particular concept in case studies, and work on comparison of concepts just starts. What is still a drawback in many papers on robustness is the missing link to include the results of the experiments again in the design

Random convex programs for distributed multi-agent consensus

We consider convex optimization problems with N randomly drawn convex constraints. Previous work has shown that the tails of the distribution of the probability that the optimal solution subject to these constraints will violate the next random constraint, can be bounded by a binomial distribution. In this paper we extend these results to the violation probability of convex combinations of optimal solutions of optimization problems with random constraints and different cost objectives. This extension has interesting applications to distributed multi-agent consensus algorithms in which the decision vectors of the agents are subject to random constraints and the agents' goal is to achieve consensus on a common value of the decision vector that satisfies the constraints. We give explicit bounds on the tails of the probability that the agents' decision vectors at an arbitrary iteration of the consensus protocol violate further constraint realizations. In a numerical experiment we apply these results to a model predictive control problem in which the agents aim to achieve consensus on a control sequence subject to random terminal constraints

Average flow constraints and stabilizability in uncertain production-distribution systems

We consider a multi-inventory system with controlled flows and uncertain demands (disturbances) bounded within assigned compact sets. The system is modelled as a first-order one integrating the discrepancy between controlled flows and demands at different sites/nodes. Thus, the buffer levels at the nodes represent the system state. Given a long-term average demand, we are interested in a control strategy that satisfies just one of two requirements: (i) meeting any possible demand at each time (worst case stability) or (ii) achieving a predefined flow in the average (average flow constraints). Necessary and sufficient conditions for the achievement of both goals have been proposed by the authors. In this paper, we face the case in which these conditions are not satisfied. We show that, if we ignore the requirement on worst case stability, we can find a control strategy driving the expected value of the state to zero. On the contrary, if we ignore the average flow constraints, we can find a control strategy that satisfies worst case stability while optimizing any linear cost on the average control. In the latter case, we provide a tight bound for the cost

Towards a Hand Exoskeleton for a Smart EVA Glove

In this paper we investigate the key factors associated with the realization of a hand exoskeleton that could be embedded in an astronaut glove for EVA (Extra Vehicular Activities). Such a project poses several and varied problems, mainly due to the complex structure of the human hand and to the extreme environment in which the glove operates. This work provides an overview of existing exoskeletons and their related technologies and lays the ground for the forthcoming prototype realization, by presenting a preliminary analysis of possible solutions in terms of mechanical structure, actuators and sensors

Autologous microsurgical breast reconstruction and coronary artery bypass grafting: an anatomical study and clinical implications

OBJECTIVE: To identify possible avenues of sparing the internal mammary artery (IMA) for coronary artery bypass grafting (CABG) in women undergoing autologous breast reconstruction with deep inferior epigastric artery perforator (DIEP) flaps. BACKGROUND: Optimal autologous reconstruction of the breast and coronary artery bypass grafting (CABG) are often mutually exclusive as they both require utilisation of the IMA as the preferred arterial conduit. Given the prevalence of both breast cancer and coronary artery disease, this is an important issue for women's health as women with DIEP flap reconstructions and women at increased risk of developing coronary artery disease are potentially restricted from receiving this reconstructive option should the other condition arise. METHODS: The largest clinical and cadaveric anatomical study (n=315) to date was performed, investigating four solutions to this predicament by correlating the precise requirements of breast reconstruction and CABG against the anatomical features of the in situ IMAs. This information was supplemented by a thorough literature review. RESULTS: Minimum lengths of the left and right IMA needed for grafting to the left-anterior descending artery are 160.08 and 177.80 mm, respectively. Based on anatomical findings, the suitable options for anastomosis to each intercostals space are offered. In addition, 87-91% of patients have IMA perforator vessels to which DIEP flaps can be anastomosed in the first- and second-intercostal spaces. CONCLUSION: We outline five methods of preserving the IMA for future CABG: (1) lowering the level of DIEP flaps to the fourth- and fifth-intercostals spaces, (2) using the DIEP pedicle as an intermediary for CABG, (3) using IMA perforators to spare the IMA proper, (4) using and end-to-side anastomosis between the DIEP pedicle and IMA and (5) anastomosis of DIEP flaps using retrograde flow from the distal IMA. With careful patient selection, we hypothesize using the IMA for autologous breast reconstruction need not be an absolute contraindication for future CABG

Large-scale unit commitment under uncertainty: an updated literature survey

The Unit Commitment problem in energy management aims at finding the optimal production schedule of a set of generation units, while meeting various system-wide constraints. It has always been a large-scale, non-convex, difficult problem, especially in view of the fact that, due to operational requirements, it has to be solved in an unreasonably small time for its size. Recently, growing renewable energy shares have strongly increased the level of uncertainty in the system, making the (ideal) Unit Commitment model a large-scale, non-convex and uncertain (stochastic, robust, chance-constrained) program. We provide a survey of the literature on methods for the Uncertain Unit Commitment problem, in all its variants. We start with a review of the main contributions on solution methods for the deterministic versions of the problem, focussing on those based on mathematical programming techniques that are more relevant for the uncertain versions of the problem. We then present and categorize the approaches to the latter, while providing entry points to the relevant literature on optimization under uncertainty. This is an updated version of the paper "Large-scale Unit Commitment under uncertainty: a literature survey" that appeared in 4OR 13(2), 115--171 (2015); this version has over 170 more citations, most of which appeared in the last three years, proving how fast the literature on uncertain Unit Commitment evolves, and therefore the interest in this subject

Acceleration of Functional Maturation and Differentiation of Neonatal Porcine Islet Cell Monolayers Shortly In Vitro Cocultured with Microencapsulated Sertoli Cells

The limited availability of cadaveric human donor pancreata as well as the incomplete success of the Edmonton protocol for human islet allografts fasten search for new sources of insulin the producing cells for substitution cell therapy of insulin-dependent diabetes mellitus (T1DM). Starting from isolated neonatal porcine pancreatic islets (NPIs), we have obtained cell monolayers that were exposed to microencapsulated monolayered Sertoli cells (ESCs) for different time periods (7, 14, 21 days). To assess the development of the cocultured cell monolayers, we have studied either endocrine cell phenotype differentiation markers or c-kit, a hematopoietic stem cell marker, has recently been involved with growth and differentiation of β-cell subpopulations in human as well as rodent animal models. ESC which were found to either accelerate maturation and differentiation of the NPIs β-cell phenotype or identify an islet cell subpopulation that was marked positively for c-kit. The insulin/c-kit positive cells might represent a new, still unknown functionally immature β-cell like element in the porcine pancreas. Acceleration of maturation and differentiation of our NPI cell monolayers might generate a potential new opportunity to develop insulin-producing cells that may suite experimental trials for cell therapy of T1DM

Impact of rehabilitation on fatigue in post-COVID-19 patients: a systematic review and meta-analysis

The post-COVID-19 syndrome may affect patients after the COVID-19 post-acute phase. In particular, the 69% of patients reported persistent fatigue at the discharge. To date, no clear data are available regarding the most effective rehabilitative approaches for the treatment of this condition. Thus, this systematic review aimed to evaluate the rehabilitation treatment’s efficacy on fatigue in post-COVID-19 patients. We systematically searched PubMed, Scopus, and Web of Science databases to find longitudinal study designs presenting: post-COVID-19 patients as participants; a rehabilitative approach aimed to reduce post-COVID-19 syndrome as intervention; and fatigue intensity assessed through an evaluation tool that quantified the perceived exertion (i.e., fatigue severity scale, FSS; Borg Scale (BS); Borg Category Ratio 10, CR10; Checklist Individual Strength (CIS) fatigue scale; FACIT (Functional Assessment of Chronic Illness Therapy) fatigue scale). The present systematic review protocol was registered on PROSPERO (registration number CRD42021284058). Out of 704 articles, 6 studies were included. Nearly all patients showed COVID-19-related fatigue, and after the rehabilitation treatment, only 17% of subjects reported the persistency of symptoms. The overall effect size reported a −1.40 decrease in Borg Category Ratio 10 with a SE of 0.05 and a 95% CI between −1.50 and −1.30 (p < 0.001). The present systematic review and meta-analysis underlines the rehabilitation role in the fatigue reduction in patients affected by post-COVID-19 syndrome

Short term outcomes of total arterial coronary revascularization in patients above 65 years: a propensity score analysis

<p>Abstract</p> <p>Background</p> <p>Despite the advantages of bilateral mammary coronary revascularization, many surgeons are still restricting this technique to the young patients. The objective of this study is to demonstrate the safety and potential advantages of bilateral mammary coronary revascularization in patients older than 65 years.</p> <p>Methods</p> <p>Group I included 415 patients older than 65 years with exclusively bilateral mammary revascularization. Using a propensity score we selected 389 patients (group II) in whom coronary bypass operations were performed using the left internal mammary artery and the great saphenous vein.</p> <p>Results</p> <p>The incidence of postoperative stroke was higher in group II (1.5% vs. 0%, P = 0.0111). The amount of postoperative blood loss was higher in group I (908 ± 757 ml vs. 800 ± 713 ml, P = 0.0405). There were no other postoperative differences between both groups.</p> <p>Conclusion</p> <p>Bilateral internal mammary artery revascularization can be safely performed in patients older than 65 years. T-graft configuration without aortic anastomosis is particularly beneficial in this age group since it avoids aortic manipulation, which is an important risk factor for postoperative stroke.</p