536 research outputs found
A review of the role of ultrasound biomicroscopy in glaucoma associated with rare diseases of the anterior segment
Ultrasound biomicroscopy is a non-invasive imaging technique, which allows high-resolution evaluation of the anatomical features of the anterior segment of the eye regardless of optical media transparency. This technique provides diagnostically significant information in vivo for the cornea, anterior chamber, chamber angle, iris, posterior chamber, zonules, ciliary body, and lens, and is of great value in assessment of the mechanisms of glaucoma onset. The purpose of this paper is to review the use of ultrasound biomicroscopy in the diagnosis and management of rare diseases of the anterior segment such as mesodermal dysgenesis of the neural crest, iridocorneal endothelial syndrome, phakomatoses, and metabolic disorders
Age class structure in SIRD models for the COVID-19 - An analysis of Tennessee data
The COVID-19 pandemic is bringing disruptive effects on the healthcare system, economy and social life of countries all over the world. Even though the elder portion of the population is the most severely affected by the coronavirus disease, the counter-measures introduced so far by the governments do not take into account age structure, and the restrictions act uniformly on the population irrespectively of age. In this paper, we introduce a SIRD model with age classes for studying the impact on the epidemic evolution of lockdown policies applied heterogeneously on the different age groups of the population. The proposed model is then applied to COVID-19 data from the state of Tennessee. The simulation results suggest that a selective lockdown, while having a lighter socioeconomic impact, may bring benefits in terms of reduction of the mortality rate that are comparable to the ones obtained by a uniform lockdown
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Quantum spin Hall effect in bound states in continuum
Moving the polarization of the incident wave along a meridian of the Poincaré sphere, experimentally we show that the coupling with the fundamental Bloch's surface waves of the mode, provide a spatially coherent, macroscopic spinmomentum locked propagation along the symmetry axes of the PhCM. This novel mechanism of light-spin manipulation enables a versatile implementation of spin-optical structures that may pave the way to novel strategies for light spin technology and photonic multiplatform implementations
Multiclass Sparse Centroids With Application to Fast Time Series Classification
In this article, we propose an efficient multiclass classification scheme based on sparse centroids classifiers. The proposed strategy exhibits linear complexity with respect to both the number of classes and the cardinality of the feature space. The classifier we introduce is based on binary space partitioning, performed by a decision tree where the assignation law at each node is defined via a sparse centroid classifier. We apply the presented strategy to the time series classification problem, showing by experimental evidence that it achieves performance comparable to that of state-of-the-art methods, but with a significantly lower classification time. The proposed technique can be an effective option in resource-constrained environments where the classification time and the computational cost are critical or, in scenarios, where real-time classification is necessary
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
A robust MPC approach for the rebalancing of mobility on demand systems
A control-oriented model for mobility-on-demand systems is here proposed. The system is first described
through dynamical stochastic state-space equations, and then suitably simplified in order to obtain a controloriented
model, on which two control strategies based on Model Predictive Control are designed. The first
strategy aims at keeping the expected value of the number of vehicles parked in stations within prescribed
bounds; the second strategy specifically accounts for stochastic fluctuations around the expected value. The
model includes the possibility of weighting the control effort, leading to control solutions that may trade off
efficiency and cost. The models and control strategies are validated over a dataset of logged trips of ToBike,
the bike-sharing systems in the city of Turin, Italy
Convex Passivity Enforcement of Linear Macromodels via Alternate Subgradient Iterations
This paper introduces a new algorithm for passivity enforcement of linear lumped macromodels in scattering form. As typical in most state of the art passivity enforcement methods, we start with an initial non-passive macromodel obtained by a Vector Fitting process, and we perturb its parameters to make it passive. The proposed scheme is based on a convex formulation of both passivity constraints and objective function for accuracy preservation, thus allowing a formal proof of convergence to the unique optimal passive macromodel. This is a distinctive feature that differentiates the new scheme with respect to most state of the art methods, which either do not guarantee convergence or are not able to provide the most accurate solution. The presented algorithm can thus be safely used for those cases for which existing techniques fail. We illustrate the advantages of proposed method on a few benchmarks
Separable Multipartite Mixed States - Operational Asymptotically Necessary and Sufficient Conditions
We introduce an operational procedure to determine, with arbitrary
probability and accuracy, optimal entanglement witness for every multipartite
entangled state. This method provides an operational criterion for separability
which is asymptotically necessary and sufficient. Our results are also
generalized to detect all different types of multipartite entanglement.Comment: 4 pages, 2 figures, submitted to Physical Review Letters. Revised
version with new calculation
A model predictive control approach to optimally devise a two-dose vaccination rollout: A case study on COVID-19 in Italy
The COVID-19 pandemic has led to the unprecedented challenge of devising massive vaccination rollouts, toward slowing down and eventually extinguishing the diffusion of the virus. The two-dose vaccination procedure, speed requirements, and the scarcity of doses, suitable spaces, and personnel, make the optimal design of such rollouts a complex problem. Mathematical modeling, which has already proved to be determinant in the early phases of the pandemic, can again be a powerful tool to assist public health authorities in optimally planning the vaccination rollout. Here, we propose a novel epidemic model tailored to COVID-19, which includes the effect of nonpharmaceutical interventions and a concurrent two-dose vaccination campaign. Then, we leverage nonlinear model predictive control to devise optimal scheduling of first and second doses, accounting both for the healthcare needs and for the socio-economic costs associated with the epidemics. We calibrate our model to the 2021 COVID-19 vaccination campaign in Italy. Specifically, once identified the epidemic parameters from officially reported data, we numerically assess the effectiveness of the obtained optimal vaccination rollouts for the two most used vaccines. Determining the optimal vaccination strategy is nontrivial, as it depends on the efficacy and duration of the first-dose partial immunization, whereby the prioritization of first doses and the delay of second doses may be effective for vaccines with sufficiently strong first-dose immunization. Our model and optimization approach provide a flexible tool that can be adopted to help devise the current COVID-19 vaccination campaign, and increase preparedness for future epidemics
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