118,325 research outputs found
Alleluja... oh finalment||| \u27l pont an fer s\u27la Stura d\u27Cherasc ant\u27l 1856. Fantasia piemonteisa d\u27l compatriot Carlin Povigna
No full textSavigliano : Tip. Racca e Bressa, 1856
Testo scritto in dialetto piemontese
https://galileodiscovery.unipd.it/discovery/fulldisplay?context=L&vid=39UPD_INST:VU1&search_scope=MyInst_and_CI&tab=Everything&docid=alma99001242201020604
Automatic-differentiated Physics-Informed Echo State Network (API-ESN)
No full textWe propose the Automatic-differentiated Physics-Informed Echo State Network (API-ESN). The network is constrained by the physical equations through the reservoir’s exact time-derivative, which is computed by automatic differentiation. As compared to the original Physics-Informed Echo State Network, the accuracy of the time-derivative is increased by up to seven orders of magnitude. This increased accuracy is key in chaotic dynamical systems, where errors grow exponentially in time. The network is showcased in the reconstruction of unmeasured (hidden) states of a chaotic system. The API-ESN eliminates a source of error, which is present in existing physics-informed echo state networks, in the computation of the time-derivative. This opens up new possibilities for an accurate reconstruction of chaotic dynamical states
Remotely Activated Nanoparticles for Anticancer Therapy
No full textThe present review highlights the importance of remotely activated nanoparticles for anticancer purposes.For each physical input, we present its possible active synergy with several nanomaterials.We report examples and the mechanism of action when clarified.Clinical trials involving remotely triggered nanoparticles are discussed. Cancer has nowadays become one of the leading causes of death worldwide. Conventional anticancer approaches are associated with different limitations. Therefore, innovative methodologies are being investigated, and several researchers propose the use of remotely activated nanoparticles to trigger cancer cell death. The idea is to conjugate two different components, i.e., an external physical input and nanoparticles. Both are given in a harmless dose that once combined together act synergistically to therapeutically treat the cell or tissue of interest, thus also limiting the negative outcomes for the surrounding tissues. Tuning both the properties of the nanomaterial and the involved triggering stimulus, it is possible furthermore to achieve not only a therapeutic effect, but also a powerful platform for imaging at the same time, obtaining a nano-theranostic application. In the present review, we highlight the role of nanoparticles as therapeutic or theranostic tools, thus excluding the cases where a molecular drug is activated. We thus present many examples where the highly cytotoxic power only derives from the active interaction between different physical inputs and nanoparticles. We perform a special focus on mechanical waves responding nanoparticles, in which remotely activated nanoparticles directly become therapeutic agents without the need of the administration of chemotherapeutics or sonosensitizing drugs. [Figure not available: see fulltext.
Robust Optimization and Validation of Echo State Networks for learning chaotic dynamics
No full textAn approach to the time-accurate prediction of chaotic solutions is by learning temporal patterns from data. Echo State Networks (ESNs), which are a class of Reservoir Computing, can accurately predict the chaotic dynamics well beyond the predictability time. Existing studies, however, also showed that small changes in the hyperparameters may markedly affect the network's performance. The overarching aim of this paper is to improve the robustness in the selection of hyperparameters in Echo State Networks for the time-accurate prediction of chaotic solutions. We define the robustness of a validation strategy as its ability to select hyperparameters that perform consistently between validation and test sets. The goal is three-fold. First, we investigate routinely used validation strategies. Second, we propose the Recycle Validation, and the chaotic versions of existing validation strategies, to specifically tackle the forecasting of chaotic systems. Third, we compare Bayesian optimization with the traditional grid search for optimal hyperparameter selection. Numerical tests are performed on prototypical nonlinear systems that have chaotic and quasiperiodic solutions, such as the Lorenz and Lorenz-96 systems, and the Kuznetsov oscillator. Both model-free and model-informed Echo State Networks are analysed. By comparing the networks’ performance in learning chaotic (unpredictable) versus quasiperiodic (predictable) solutions, we highlight fundamental challenges in learning chaotic solutions. The proposed validation strategies, which are based on the dynamical systems properties of chaotic time series, are shown to outperform the state-of-the-art validation strategies. Because the strategies are principled – they are based on chaos theory such as the Lyapunov time – they can be applied to other Recurrent Neural Networks architectures with little modification. This work opens up new possibilities for the robust design and application of Echo State Networks, and Recurrent Neural Networks, to the time-accurate prediction of chaotic systems
Correction to: Remotely Activated Nanoparticles for Anticancer Therapy (Nano-Micro Letters, (2021), 13 (11), 10.1007/s40820-020-00537-8)
No full textIn the original publication figures 7 and 11 need to be updated with correct values. The correct version of figures 7 and 11 is provided in this correction. The original article has been corrected
Data-driven prediction and control of extreme events in a chaotic flow
No full textAn extreme event is a sudden and violent change in the state of a nonlinear system. In fluid dynamics, extreme events can have adverse effects on the system's optimal design and operability, which calls for accurate methods for their prediction and control. In this paper, we propose a data-driven methodology for the prediction and control of extreme events in a chaotic shear flow. The approach is based on echo state networks, which are a type of reservoir computing that learn temporal correlations within a time-dependent data set. The objective is fivefold. First, we exploit ad hoc metrics from binary classification to analyze (1) how many of the extreme events predicted by the network actually occur in the test set (precision) and (2) how many extreme events are missed by the network (recall). We apply a principled strategy for optimal hyperparameter selection, which is key to the networks' performance. Second, we focus on the time-accurate prediction of extreme events. We show that echo state networks are able to predict extreme events well beyond the predictability time, i.e., up to more than five Lyapunov times. Third, we focus on the long-term prediction of extreme events from a statistical point of view. By training the networks with data sets that contain nonconverged statistics, we show that the networks are able to learn and extrapolate the flow's long-term statistics. In other words, the networks are able to extrapolate in time from relatively short time series. Fourth, we design a simple and effective control strategy to prevent extreme events from occurring. The control strategy decreases the occurrence of extreme events up to one order of magnitude with respect to the uncontrolled system. Finally, we analyze the robustness of the results for a range of Reynolds numbers. We show that the networks perform well across a wide range of regimes. This work opens up new possibilities for the data-driven prediction and control of extreme events in chaotic systems
Lipid-coated zinc oxide nanocrystals as innovative ROS-generators for photodynamic therapy
No full textPhotodynamic Therapy (PDT) is a medical treatment that combines the administration of a nontoxic drug, called photosensitizer (PS), with light irradiation of the targeted region. It has been proposed as a new cancer therapy, promising better selectivity and fewer side-effects compared to traditional chemo- and radio-therapies. PSs indeed can accumulate specifically within the region of interest so that when the light is directly focused only in that region the therapeutic effect is highly localized. Traditional PSs, like chlorins and porphyrins, suffer from several drawbacks such as aggregation in biological media and poor biocompatibility. Thus, the development of innovative photosensitizers able to overcome these issues is crucial to the therapeutic action of PDT. Among the others, nanostructured Zinc Oxide (ZnO) has been recently proposed as new therapeutic agent and PS thanks to its semiconducting properties, biocompatible features, and ease of functionalization [1]. Nevertheless, further efforts are needed in order to improve its colloidal stability in biological media and to unravel the effective therapeutic mechanism. Here, we propose the synthesis and characterization of lipid-coated ZnO nanoparticles as new photosensitizer for cancer PDT [2]. First, by Dynamic Light Scattering (DLS) experiments, we show that the lipid-coating increases the colloidal stability of the ZnO NPs in Phosphate buffered saline (PBS). Then, using Electron Paramagnetic Resonance (EPR) coupled with the spin-trapping technique, we demonstrate and characterize the ability of bare and lipid-coated ZnO NPs to generate Reactive Oxygen Species (ROS) in water only when remotely actuated via light irradiation. Interestingly, our results aware that the surface chemistry of the NPs greatly influence the type of photo-generated ROS. Finally, we show that our NPs are effectively internalized inside human epithelial carcinoma cells (HeLa) via a lysosomal pathway and that they are able to generate ROS inside cancer cells. [1] B. Dumontel, M. Canta, H. Engelke, A. Chiodoni, L. Racca, A. Ancona, T. Limongi, G. Canavese and V. Cauda, J. Mater. Chem. B. under revision. [2] A. Ancona, H. Engelke, N. Garino, B. Dumontel, W.Fazzini and V. Cauda, to be submitted. The support from ERC Starting Grant - Project N. 678151 "Trojananohorse" is gratefully acknowledged
Il sesto provvedimento "anticrisi" dell'era Monti presenta soluzioni più adatte alle reali esigenze
No full textPubblicato sul supplemento n. 129/L alla Gazzetta Ufficiale del 26 giugno 2012, il decreto-legge n. 83 del 22 giugno 2012 è il sesto decreto anti-crisi del Governo Monti. Come si legge nel preambolo, il nuovo corposo ed eterogeneo decreto nasce dall'urgenza di emanare disposizioni dirette a favorire la crescita, lo sviluppo e la competitività nei settori delle infrastrutture, dell'edilizia e dei trasporti e di dare un assetto più ordinato agli incentivi per le imprese. Tenta di azionare più leve possibili per risollevare l'economia nazionale, facendo largo uso dei suggerimenti proposti dalle categorie interessate. Cerca di stimolare in particolar modo l'edilizia pubblica e privata, come volano diffuso di crescita. Detta anche misure per l'agenda digitale e la trasparenza nella pubblica amministrazione, per lo sviluppo e il rafforzamento del settore energetico, per accelerare l'apertura dei servizi pubblici locali al mercato, per accorciare i tempi della giustizia civile, per l'occupazione giovanile nella green economy e per le imprese nel settore agricolo, per la ricerca scientifica e tecnologica e per il turismo e lo sport
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