A Feedback-Based Regularized Primal-Dual Gradient Method for Time-Varying Nonconvex Optimization

This paper considers time-varying nonconvex optimization problems, utilized to model optimal operational trajectories of systems governed by possibly nonlinear physical or logical models. Algorithms for tracking a Karush-Kuhn-Tucker point are synthesized, based on a regularized primal-dual gradient method. In particular, the paper proposes a feedback-based primal-dual gradient algorithm, where analytical models for system state or constraints are replaced with actual measurements. When cost and constraint functions are twice continuously differentiable, conditions for the proposed algorithms to have bounded tracking error are derived, and a discussion of their practical implications is provided. Illustrative numerical simulations are presented for an application in power systems

A Feedback-Based Regularized Primal-Dual Gradient Method for Time-Varying Nonconvex Optimization

This paper considers time-varying nonconvex optimization problems, utilized to model optimal operational trajectories of systems governed by possibly nonlinear physical or logical models. Algorithms for tracking a Karush-Kuhn-Tucker point are synthesized, based on a regularized primal-dual gradient method. In particular, the paper proposes a feedback-based primal-dual gradient algorithm, where analytical models for system state or constraints are replaced with actual measurements. When cost and constraint functions are twice continuously differentiable, conditions for the proposed algorithms to have bounded tracking error are derived, and a discussion of their practical implications is provided. Illustrative numerical simulations are presented for an application in power systems

Discovering Communities for Microgrids with Spatial-Temporal Net Energy

Smart grid has integrated an increasing number of distributed energy resources to improve the efficiency and flexibility of power generation and consumption as well as the resilience of the power grid. The energy consumers on the power grid, e.g., households, equipped with distributed energy resources can be considered as “microgrids” that both generate and consume electricity. In this paper, we study the energy community discovery problems which identify energy communities for the microgrids to facilitate energy management, e.g., load balancing, energy sharing and trading on the grid. Specifically, we present efficient algorithms to discover such communities of microgrids considering both their geo-locations and net energy (NE) over any period. Finally, we experimentally validate the performance of the algorithms using both synthetic and real datasets

Autosomal dominant polycystic kidney disease in young adults

Background The clinical manifestations of autosomal dominant polycystic kidney disease (ADPKD) usually appear in adulthood, however pediatric series report a high morbidity. The objective of the study was to analyze the clinical characteristics of ADPKD in young adults. Methods Family history, hypertension, albuminuria, estimated glomerular filtration rate (eGFR) and imaging tests were examined in 346 young adults (18-30 years old) out of 2521 patients in the Spanish ADPKD registry (REPQRAD). A literature review searched for reports on hypertension in series with more than 50 young (age <30 years) ADPKD patients. Results The mean age of this young adult cohort was 25.24 (SD 3.72) years. The mean age at diagnosis of hypertension was 21.15 (SD 4.62) years, while in the overall REPQRAD population was aged 37.6 years. The prevalence of hypertension was 28.03% and increased with age (18-24 years, 16.8%; 25-30 years, 36.8%). Although prevalence was lower in women than in men, the age at onset of hypertension (21 years) was similar in both sexes. Mean eGFR was 108 (SD 21) mL/min/1.73 m(2), 38.0% had liver cysts and 3.45% of those studied had intracranial aneurysms. In multivariate analyses, hematuria episodes and kidney length were independent predictors of hypertension (area under the curve 0.75). The prevalence of hypertension in 22 pediatric cohorts was 20%-40%, but no literature reports on hypertension in young ADPKD adults were found. Conclusions Young adults present non-negligible ADPKD-related morbidity. This supports the need for a thorough assessment of young adults at risk of ADPKD that allows early diagnosis and treatment of hypertension. Lay Summary Impairment of renal function usually develops from the fourth decade of life in autosomal dominant polycystic kidney disease (ADPKD). However, hypertension precedes the onset of renal insufficiency. In published pediatric series, the prevalence of hypertension is 20%-40%. However, clinical information on young adults with ADPKD is scarce. We present the largest cohort of young adults (age 18-30 years) with ADPKD published to date. Prevalence of hypertension is 28% and increases with age, reaching 36.8% in the subgroup aged 25-30 years, despite normal glomerular filtration rate and albuminuria. The prevalence of hypertension is higher in males, but the mean age at diagnosis (21 years) was similar in both sexes. Young adults present non-negligible ADPKD-related morbidity. This supports the need for a thorough assessment that allows early diagnosis and treatment of hypertension, before decline of estimated glomerular filtration rate. Ambulatory blood pressure monitoring may be especially useful in this regard.11 página

Il bilancio integrato per le PMI

Accanto ai capitali finanziario e produttivo, ogni impresa fonda il proprio business e il proprio successo anche su risorse intangibili, quali il capitale intellettuale, il capitale umano, il capitale sociale e relazionale ed il capitale naturale. Il tradizionale bilancio economico-finanziario, però, non è adatto a valutare e rappresentare tali risorse, poiché è stato concepito con riferimento ad un’economia industriale fondata pressoché esclusivamente su capitali tangibili; pertanto, anche avuto riguardo alla realtà delle PMI, si rende oggi necessario introdurre nuovi strumenti e nuovi indicatori per la misurazione e la rendicontazione, che siano in grado di cogliere e valorizzare anche le componenti immateriali del capitale aziendale. In questo contesto, il bilancio integrato si pone come una forma evoluta di comunicazione aziendale, finalizzata ad illustrare come strategia, governance, modello di business, rapporti con gli stakeholder, performance passate e prospettive future, rischi e opportunità consentano anche ad un’impresa di piccole e medie dimensioni di creare valore nel breve, medio e lungo termine

Group sparse Lasso for cognitive network sensing robust to model uncertainties and outliers

PostprintTo account for variations in the frequency, time, and space dimensions, dynamic re-use of licensed bands under the cognitive radio (CR) paradigm calls for innovative network-level sensing algorithms for multi-dimensional spectrum opportunity awareness. Toward this direction, the present paper develops a collaborative scheme whereby CRs cooperate to localize active primary user (PU) transmitters and reconstruct a power spectral density (PSD) map portraying the spatial distribution of power across the monitored area per frequency band and channel coherence interval. The sensing scheme is based on a parsimonious model that accounts for two forms of sparsity: one due to the narrow-band nature of transmit-PSDs compared to the large portion of spectrum that a CR can sense, and another one emerging when adopting a spatial grid of candidate PU locations. Capitalizing on this dual sparsity, an estimator of the model coefficients is obtained based on the group sparse least-absolute-shrinkage-and-selection operator (GS-Lasso). A novel reduced-complexity GS-Lasso solver is developed by resorting to the alternating direction method of multipliers (ADMoM). Robust versions of this GS-Lasso estimator are also introduced using a GS total least-squares (TLS) approach to cope with both uncertainty in the regression matrices, arising due to inaccurate channel estimation and grid-mismatch effects, and unexpected model outliers. In spite of the non-convexity of the GS-TLS criterion, the novel robust algorithm has guaranteed convergence to (at least) a local optimum. The analytical findings are corroborated by numerical test

Renewable-based charging in green ride-sharing

Abstract Governments, regulatory bodies, and manufacturers are proposing plans to accelerate the adoption of electric vehicles (EVs), with the goal of reducing the impact of greenhouse gases and pollutants from internal combustion engines on human health and climate change. In this context, the paper considers a scenario where ride-sharing enterprises utilize a 100%-electrified fleet of vehicles, and seeks responses to the following key question: How can renewable-based EV charging be maximized without disrupting the quality of the ride-sharing services? We propose a new mechanism to promote EV charging during hours of high renewable generation, and we introduce the concept of charge request, which is issued by a power utility company. Our mechanism is inspired by a game-theoretic approach where the power utility company proposes incentives and the ride-sharing platform assigns vehicles to both ride and charge requests; the bargaining mechanism leads to prices and EV assignments that are aligned with the notion of Nash equilibria. Numerical results show that it is possible to shift the EV charging during periods of high renewable generation and adapt to intermittent generation while minimizing the impact on the quality of service. The paper also investigates how the users’ willingness to ride-share affects the charging strategy and the quality of service