Solution Map Analysis of a Multiscale Drift-Diffusion Model for Organic Solar Cells

In this article we address the theoretical study of a multiscale drift-diffusion (DD) model for the description of photoconversion mechanisms in organic solar cells. The multiscale nature of the formulation is based on the co-presence of light absorption, conversion and diffusion phenomena that occur in the three-dimensional material bulk, of charge photoconversion phenomena that occur at the two-dimensional material interface separating acceptor and donor material phases, and of charge separation and subsequent charge transport in each three-dimensional material phase to device terminals that are driven by drift and diffusion electrical forces. The model accounts for the nonlinear interaction among four species: excitons, polarons, electrons and holes, and allows to quantitatively predict the electrical current collected at the device contacts of the cell. Existence and uniqueness of weak solutions of the DD system, as well as nonnegativity of all species concentrations, are proved in the stationary regime via a solution map that is a variant of the Gummel iteration commonly used in the treatment of the DD model for inorganic semiconductors. The results are established upon assuming suitable restrictions on the data and some regularity property on the mixed boundary value problem for the Poisson equation. The theoretical conclusions are numerically validated on the simulation of three-dimensional problems characterized by realistic values of the physical parameters

CO2 Monitor: Progettazione e sviluppo di un sistema IoT per monitoraggio della CO2 e prevenzione Covid in ambienti indoor

Rimasti segnati dalla pandemia globale scatenatasi all’inizio del 2020, medici e comunità scientifiche di tutto il mondo hanno posto l’accento su tematiche già da tempo conosciute, quali l’importanza della qualità dell’aria che tutti i giorni respiriamo. L’introduzione di misure anti contagio ha infatti spinto gli studiosi ad effettuare ricerche ulteriori circa la IAQ con lo scopo di prevenire i contagi all’interno di ambienti chiusi quotidianamente frequentati, quali scuole, uffici, ed abitazioni private. Basandosi su tali premesse, la tesi ha investigato la progettazione e realizzazione prototipale di un dispositivo IoT per rilevare i livelli di CO2 nell’aria e inviare notifiche quando il valore supera la soglia di sicurezza. È stata sviluppata un’applicazione mobile connessa al sistema per monitorare le misurazioni in tempo reale e storicizzarle. Assieme a questi due componenti, sono stati implementati altri moduli software al fine di permettere un funzio- namento agevole del sistema: un server di raccolta dati, uno strato di API e due database. Il sistema è stato validato considerando diversi scenari d’uso. La realizzazione del dispositivo e dell’intero sistema software correlato si inserisce nel contesto dell’innovazione tecnologica per il monitoraggio della qualità dell’aria in spazi indoor

Supporting Sustainable Virtual Network Mutations with Mystique

The abiding attempt of automation has also permeated the networks, with the ability to measure, analyze, and control themselves in an automated manner, by reacting to changes in the environment (e.g., demand). When provided with these features, networks are often labeled as "self-driving" or "autonomous". In this regard, the provision and orchestration of physical or virtual resources are crucial for both Quality of Service (QoS) guarantees and cost management in the edge/cloud computing environment. To effectively manage the lifecycle of these resources, an auto-scaling mechanism is essential. However, traditional threshold-based and recent Machine Learning (ML)-based policies are often unable to address the soaring complexity of networks due to their centralized approach. By relying on multi-agent reinforcement learning, we propose Mystique, a solution that learns from the load on links to establish the minimal set of active network resources. As traffic demands ebb and flow, our adaptive and self-driving solution can scale up and down and also react to failures in a fully automated, flexible, and efficient manner. Our results demonstrate that the presented solution can reduce network energy consumption while providing an adequate service level, outperforming other benchmark auto-scaling approaches

An architecture for adaptive task planning in support of IoT-based machine learning applications for disaster scenarios

The proliferation of the Internet of Things (IoT) in conjunction with edge computing has recently opened up several possibilities for several new applications. Typical examples are Unmanned Aerial Vehicles (UAV) that are deployed for rapid disaster response, photogrammetry, surveillance, and environmental monitoring. To support the flourishing development of Machine Learning assisted applications across all these networked applications, a common challenge is the provision of a persistent service, i.e., a service capable of consistently maintaining a high level of performance, facing possible failures. To address these service resilient challenges, we propose APRON, an edge solution for distributed and adaptive task planning management in a network of IoT devices, e.g., drones. Exploiting Jackson's network model, our architecture applies a novel planning strategy to better support control and monitoring operations while the states of the network evolve. To demonstrate the functionalities of our architecture, we also implemented a deep-learning based audio-recognition application using the APRON NorthBound interface, to detect human voices in challenged networks. The application's logic uses Transfer Learning to improve the audio classification accuracy and the runtime of the UAV-based rescue operations

Owl: Congestion Control with Partially Invisible Networks via Reinforcement Learning

Years of research on transport protocols have not solved the tussle between in-network and end-to-end congestion control. This debate is due to the variance of conditions and assumptions in different network scenarios, e.g., cellular versus data center networks. Recently, the community has proposed a few transport protocols driven by machine learning, nonetheless limited to end-to-end approaches. In this paper, we present Owl, a transport protocol based on reinforcement learning, whose goal is to select the proper congestion window learning from end-to-end features and network signals, when available. We show that our solution converges to a fair resource allocation after the learning overhead. Our kernel implementation, deployed over emulated and large scale virtual network testbeds, outperforms all benchmark solutions based on end-to-end or in-network congestion control

Wasserstein Actor-Critic: Directed Exploration via Optimism for Continuous-Actions Control

Uncertainty quantification has been extensively used as a means to achieve efficient directed exploration in Reinforcement Learning (RL). However, state-of-the-art methods for continuous actions still suffer from high sample complexity requirements. Indeed, they either completely lack strategies for propagating the epistemic uncertainty throughout the updates, or they mix it with aleatoric uncertainty while learning the full return distribution (e.g., distributional RL). In this paper, we propose Wasserstein Actor-Critic (WAC), an actor-critic architecture inspired by the recent Wasserstein Q-Learning (WQL), that employs approximate Q-posteriors to represent the epistemic uncertainty and Wasserstein barycenters for uncertainty propagation across the state-action space. WAC enforces exploration in a principled way by guiding the policy learning process with the optimization of an upper bound of the Q-value estimates. Furthermore, we study some peculiar issues that arise when using function approximation, coupled with the uncertainty estimation, and propose a regularized loss for the uncertainty estimation. Finally, we evaluate our algorithm on standard MujoCo tasks as well as suite of continuous-actions domains, where exploration is crucial, in comparison with state-of-the-art baselines. Additional details and results can be found in the supplementary material with our Arxiv preprint

Partially Oblivious Congestion Control for the Internet via Reinforcement Learning

Despite years of research on transport protocols, the tussle between in-network and end-to-end congestion control has not been solved. This debate is due to the variance of conditions and assumptions in different network scenarios, e.g., cellular versus data center networks. Recently, the community has proposed a few transport protocols driven by machine learning, nonetheless limited to end-to-end approaches. In this paper, we present Owl, a transport protocol based on reinforcement learning, whose goal is to select the proper congestion window learning from end-to-end features and network signals, when available. We show that our solution converges to a fair resource allocation after the learning overhead. Our kernel implementation, deployed over emulated and large scale virtual network testbeds, outperforms all benchmark solutions based on end-to-end or in-network congestion control

VITOM®-3D in lumbar disc herniation: Preliminary experience

Objectives: In neurosurgery, optimal illumination and surgeon view's magnification are essential to perform delicate and dangerous operations, such as aneurysms clipping and tumors removal. In this paper, the authors report their initial experience using a 3D-exoscope in spinal surgical procedures. Patients and methods: From January to July 2018 we performed 9 lumbar discectomies using a VITOM®-3D exoscope. We decided to examine these cases, with particular attention to the surgical timing and to the postoperative results in terms of pain control (VAS). Patient positioning, surgical instruments and approach technique were essentially the same used routinely in standard spinal disc herniation surgery.A "control" group composed of 9 cases was selected from patients who underwent a standard discectomy during the same period with the same neurosurgeons in order to obtain two homogeneous and comparable populations. Results: The length of operative time was measured and appeared to be longer in exoscope-assisted discectomies than in the traditional procedures (average 160 min vs 133 min); moreover the one-month postoperative VAS of the two groups were collected and compared but, after a statistical analysis, these differences resulted to be not statistically significant. No technical difficulties or surgical complications were noted. Conclusions: Despite the limited group of patients, the VITOM®-3D exoscope can be considered an interesting instrument in spinal procedures. It cannot permanently substitute the operating microscope but it shows interesting characteristics that make it a useful tool for surgeon's comfort and a versatile and relatively economic instrument in the neurosurgical armamentarium, as a part of a 3D working station composed by endoscope and exoscope. Keywords: Exoscope, Lumbar disc herniation, VITOM®-3D, Discectom

Comparative transcriptomic profiling of two tomato lines with different ascorbate content in tomato fruit.

In recent years, interest in tomato breeding for enhanced antioxidant content has increased as medical research has pointed to human health benefits from antioxidant dietary intake. Ascorbate is one of the major antioxidants present in tomato, and little is known about mechanisms governing ascorbate pool size in this fruit. In order to provide further insights into genetic mechanisms controlling ascorbate biosynthesis and accumulation in tomato, we investigated the fruit transcriptome profile of the Solanum pennellii introgression line 10-1 that exhibits a lower fruit ascorbate level than its cultivated parental genotype. Our results showed that this reduced ascorbate level is associated with an increased antioxidant demand arising from an accelerated oxidative metabolism mainly involving mitochondria, peroxisomes, and cytoplasm. Candidate genes for controlling ascorbate level in tomato fruit were identified, highlighting the role of glycolysis, glyoxylate metabolism, and purine breakdown in modulating the ascorbate pool size