A robust multilevel approximate inverse preconditioner for symmetric positive definite matrices

The use of factorized sparse approximate inverse (FSAI) preconditioners in a standard multilevel framework for symmetric positive definite (SPD) matrices may pose a number of issues as to the definiteness of the Schur complement at each level. The present work introduces a robust multilevel approach for SPD problems based on FSAI preconditioning, which eliminates the chance of algorithmic breakdowns independently of the preconditioner sparsity. The multilevel FSAI algorithm is further enhanced by introducing descending and ascending low-rank corrections, thus giving rise to the multilevel FSAI with low-rank corrections (MFLR) preconditioner. The proposed algorithm is investigated in a number of test problems. The numerical results show that the MFLR preconditioner is a robust approach that can significantly accelerate the solver convergence rate preserving a good degree of parallelism. The possibly large set-up cost, mainly due to the computation of the eigenpairs needed by low-rank corrections, makes its use attractive in applications where the preconditioner can be recycled along a number of linear solves

Sensitivity of Pollutant Concentrations to the Turbulence Schemes of a Dispersion Modelling Chain over Complex Orography

Atmospheric circulation over mountainous regions is more complex than over flat terrain due to the interaction of flows on various scales: synoptic-scale flows, thermally-driven mesoscale winds and turbulent fluxes. In order to faithfully reconstruct the circulation affecting the dispersion and deposition of pollutants in mountainous areas, meteorological models should have a sub-kilometer grid spacing, where turbulent motions are partially resolved and the parametrizations of the sub-grid scale fluxes need to be evaluated. In this study, a modelling chain based on the Weather Research and Forecasting (WRF) model and the chemical transport model Flexible Air Quality Regional Model (FARM) is applied to estimate the pollutant concentrations at a 0.5 km horizontal resolution over the Aosta Valley, a mountainous region of the northwestern Alps. Two pollution episodes that occurred in this region are reconstructed: one summer episode dominated by thermally-driven winds, and one winter episode dominated by synoptic-scale flows. Three WRF configurations with specific planetary boundary layer and surface layer schemes are tested, and the numerical results are compared with the surface measurements of meteorological variables at twenty-four stations. For each WRF configuration, two different FARM runs are performed, with turbulence-related quantities provided by the SURface-atmosphere interFace PROcessor or directly by WRF. The chemical concentrations resulting from the different FARM runs are compared with the surface measurements of particulate matter of less than 10 µm in diameter and nitrogen dioxide taken at five air quality stations. Furthermore, these results are compared with the outputs of the modelling chain employed routinely by the Aosta Valley Environmental Protection Agency, based on FARM driven by COSMO-I2 (COnsortium for Small-scale MOdelling) at 2.8 km horizontal grid spacing. The pollution events are underestimated by the modelling chain, but the bias between simulated and measured surface concentrations is reduced using the configuration based on WRF turbulence parametrizations, which imply a reduced dispersion

Intelligenza artificiale e sicurezza: opportunità, rischi e raccomandazioni

L'IA (o intelligenza artificiale) è una disciplina in forte espansione negli ultimi anni e lo sarà sempre più nel prossimo futuro: tuttavia è dal 1956 che l’IA studia l’emulazione dell’intelligenza da parte delle macchine, intese come software e in certi casi hardware. L’IA è nata dall’idea di costruire macchine che - ispirandosi ai processi legati all’intelligenza umana - siano in grado di risolvere problemi complessi, per i quali solitamente si ritiene che sia necessario un qualche tipo di ragionamento intelligente. La principale area di ricerca e applicazione attuale dell’IA è il machine learning (algoritmi che imparano e si adattano in base ai dati che ricevono), che negli ultimi anni ha trovato ampie applicazioni grazie alle reti neurali (modelli matematici composti da neuroni artificiali) che a loro volta hanno consentito la nascita del deep learning (reti neurali di maggiore complessità). Appartengono al mondo dell’IA anche i sistemi esperti, la visione artificiale, il riconoscimento vocale, l’elaborazione del linguaggio naturale, la robotica avanzata e alcune soluzioni di cybersecurity. Quando si parla di IA c'è chi ne è entusiasta pensando alle opportunità, altri sono preoccupati poiché temono tecnologie futuristiche di un mondo in cui i robot sostituiranno l'uomo, gli toglieranno il lavoro e decideranno al suo posto. In realtà l'IA è ampiamente utilizzata già oggi in molti campi, ad esempio nei cellulari, negli oggetti smart (IoT), nelle industry 4.0, per le smart city, nei sistemi di sicurezza informatica, nei sistemi di guida autonoma (drive o parking assistant), nei chat bot di vari siti web; questi sono solo alcuni esempi basati tutti su algoritmi tipici dell’intelligenza artificiale. Grazie all'IA le aziende possono avere svariati vantaggi nel fornire servizi avanzati, personalizzati, prevedere trend, anticipare le scelte degli utenti, ecc. Ma non è tutto oro quel che luccica: ci sono talvolta problemi tecnici, interrogativi etici, rischi di sicurezza, norme e legislazioni non del tutto chiare. Le organizzazioni che già adottano soluzioni basate sull’IA, o quelle che intendono farlo, potrebbero beneficiare di questa pubblicazione per approfondirne le opportunità, i rischi e le relative contromisure. La Community for Security del Clusit si augura che questa pubblicazione possa fornire ai lettori un utile quadro d’insieme di una realtà, come l’intelligenza artificiale, che ci accompagnerà sempre più nella vita personale, sociale e lavorativa.AI (or artificial intelligence) is a booming discipline in recent years and will be increasingly so in the near future.However, it is since 1956 that AI has been studying the emulation of intelligence by machines, understood as software and in some cases hardware. AI arose from the idea of building machines that-inspired by processes related to human intelligence-are able to solve complex problems, for which it is usually believed that some kind of intelligent reasoning is required. The main current area of AI research and application is machine learning (algorithms that learn and adapt based on the data they receive), which has found wide applications in recent years thanks to neural networks (mathematical models composed of artificial neurons), which in turn have enabled the emergence of deep learning (neural networks of greater complexity). Also belonging to the AI world are expert systems, computer vision, speech recognition, natural language processing, advanced robotics and some cybersecurity solutions. When it comes to AI there are those who are enthusiastic about it thinking of the opportunities, others are concerned as they fear futuristic technologies of a world where robots will replace humans, take away their jobs and make decisions for them. In reality, AI is already widely used in many fields, for example, in cell phones, smart objects (IoT), industries 4.0, for smart cities, cybersecurity systems, autonomous driving systems (drive or parking assistant), chat bots on various websites; these are just a few examples all based on typical artificial intelligence algorithms. Thanks to AI, companies can have a variety of advantages in providing advanced, personalized services, predicting trends, anticipating user choices, etc. But not all that glitters is gold: there are sometimes technical problems, ethical questions, security risks, and standards and legislation that are not entirely clear. Organizations already adopting AI-based solutions, or those planning to do so, could benefit from this publication to learn more about the opportunities, risks, and related countermeasures. Clusit's Community for Security hopes that this publication will provide readers with a useful overview of a reality, such as artificial intelligence, that will increasingly accompany us in our personal, social and working lives

Applicazione di un sistema di automazione per la termoregolazione e contabilizzazione del calore

Il miglioramento della prestazione energetica del patrimonio edilizio esistente è un tema sempre più attuale a livello europeo ai fini del contenimento dei consumi energetici e delle emissioni in atmosfera. Tra gli interventi di efficientamento energetico maggiormente impiegati per gli impianti centralizzati di riscaldamento ad acqua calda vi è l’installazione di dispositivi di termoregolazione e contabilizzazione del calore. Rispetto all’installazione di valvole termostatiche manuali associate a ripartitori per la contabilizzazione del calore, l’installazione di valvole termostatiche motorizzate associate a un sistema informativo di contabilizzazione del calore pare più promettente sia perché un utente informato in tempo reale sull’andamento dei propri interviene direttamente per limitarli sia perché la qualità e la quantità di dati rilevati dal sistema informativo consente di verificare ed eventualmente modificare le modalità di conduzione dell’impianto. Nel presente lavoro vengono esaminati i vantaggi ottenuti installando valvole termostatiche motorizzate associate a un sistema informativo di contabilizzazione in un edificio multifamiliare sito in un comune del Nord Italia

Implementation of an AI ready BACS system in Treviso school with DCV (Demand Control Ventilation)

The pandemic has highlighted the extreme importance of indoor air quality (IAQ). Especially in schools, so places that have always been critical for the spread of highly contagious viral diseases, IAQ is an increasingly necessary need. In the province of Treviso, a tailored system has been installed: there are both winter and summer air conditioning, plus AHU’s capable of guaranteeing the necessary air exchange to obtain a healthy level of the air inside the classrooms. The county also intends to replicate this experience in all schools of its territory and monitor the operation of these plants continuously. The important matter is not only to implement systems equipped with HRV (heat recovery ventilation), but also to equip them with automation and control systems capable of implementing an high automation standard according to EN 15232-1 and a high SRI or Smart Readiness Indicator defined by the EPBD III directive 844/2018. A high index ensures an implementation of a distributed intelligence system all over the plant, connected to the cloud and capable of extrapolating the physical variables, then processing them with ML (machine learning) and AI (artificial intelligence) techniques optimized for the HVAC algorithms. At the end, it is not enough to detect quantities such as EI (environmental index) but it is also necessary to process the data for the purposes of predictive maintenance and much more

Recent advancements in preconditioning techniques for large size linear systems suited for high performance computing

The numerical simulations of real-world engineering problems create models with several millions or even billions of degrees of freedom. Most of these simulations are centered on the solution of systems of non-linear equations, that, once linearized, become a sequence of linear systems, whose solution is often the most time-demanding task. Thus, in order to increase the capability of modeling larger cases, it is of paramount importance to exploit the resources of High Performance Computing architectures. In this framework, the development of new algorithms to accelerate the solution of linear systems for many-core architectures is a really active research field. Our main focus is algebraic preconditioning and, among the various options, we elect to develop approximate inverses for symmetric and positive definite (SPD) linear systems, both as stand-alone preconditioner or smoother for AMG techniques. This choice is mainly supported by the almost perfect parallelism that intrinsically characterizes these algorithms. As basic kernel, the Factorized Sparse Approximate Inverse (FSAI) developed in its adaptive form by Janna and Ferronato is selected. Recent developments are i) a robust multilevel approach for SPD problems based on FSAI preconditioning, which eliminates the chance of algorithmic breakdowns independently of the preconditioner sparsity and ii) a novel AMG approach featuring the adaptive FSAI method as a flexible smoother as well as new approaches to adaptively compute the prolongation operator. In this latter work, a new technique to build the prolongation is also presented

Sensitivity of Pollutant Concentrations to the Turbulence Schemes of a Dispersion Modelling Chain over Complex Orography

Atmospheric circulation over mountainous regions is more complex than over flat terrain due to the interaction of flows on various scales: synoptic-scale flows, thermally-driven mesoscale winds and turbulent fluxes. In order to faithfully reconstruct the circulation affecting the dispersion and deposition of pollutants in mountainous areas, meteorological models should have a sub-kilometer grid spacing, where turbulent motions are partially resolved and the parametrizations of the sub-grid scale fluxes need to be evaluated. In this study, a modelling chain based on the Weather Research and Forecasting (WRF) model and the chemical transport model Flexible Air Quality Regional Model (FARM) is applied to estimate the pollutant concentrations at a 0.5 km horizontal resolution over the Aosta Valley, a mountainous region of the northwestern Alps. Two pollution episodes that occurred in this region are reconstructed: one summer episode dominated by thermally-driven winds, and one winter episode dominated by synoptic-scale flows. Three WRF configurations with specific planetary boundary layer and surface layer schemes are tested, and the numerical results are compared with the surface measurements of meteorological variables at twenty-four stations. For each WRF configuration, two different FARM runs are performed, with turbulence-related quantities provided by the SURface-atmosphere interFace PROcessor or directly by WRF. The chemical concentrations resulting from the different FARM runs are compared with the surface measurements of particulate matter of less than 10 µm in diameter and nitrogen dioxide taken at five air quality stations. Furthermore, these results are compared with the outputs of the modelling chain employed routinely by the Aosta Valley Environmental Protection Agency, based on FARM driven by COSMO-I2 (COnsortium for Small-scale MOdelling) at 2.8 km horizontal grid spacing. The pollution events are underestimated by the modelling chain, but the bias between simulated and measured surface concentrations is reduced using the configuration based on WRF turbulence parametrizations, which imply a reduced dispersion

Near-infrared spectroscopy in neonatal intensive care unit: do we make our life more difficult?

The question has been the following: can the regional oxygenation monitoring change our clinical practices in neonatal intensive care? Fifty newborns of gestational age ≤ 32 weeks were recruited for regional oxygenation continuous monitoring immediately after their admission. Of these newborns 44 showed a patent ductus arteriosus (PDA) with a left to right shunt. In these subjects, a progressive decrease of the renal oxygenation (rSO2) up to values of 59.6 ± 3.6% and an increase of the renal oxygen extraction fraction (rFTOE) to 50.9 ± 3 were observed during the first hours of monitoring. The cerebral oxygenation (cSO2) instead, remained relatively constant at 64.5(± 4.2%)-69.7(± 5.6%) with a cerebral oxygen extraction fraction (cFTOE) between 28.6 ± 4.7 and 24.6 ± 6.5. Renal oxygenation improved in almost all the subjects, except that in three, up to values of rSO2 of 75(± 1.0%)-82.2(± 4.9) with a rFTOE of 20.1(± 14.8)-13.4(± 3.5) after a three-six hours treatment with dopamine at 5-7.5 μg/kg/min. These data, together with echodoppler findings, have allowed us to modify our approach to the newborn with PDA and the left-right shunt. It now consists in using dopamine as soon as ductal shunt has been left to right and waiting until the hemodynamic stability persists or until the end of the first week of life prior to consider the closure of the duct by cyclooxygenase inhibitors. Besides, 42 newborns with a post-natal age ≥ 2 weeks were selected and submitted to a regional oxygenation monitoring once hematocrit had been less than 30%. Sixteen out of 42 newborns showed a decline of rSO2 to 50 ± 5% and a rFTOE of 45 ± 3, with a cSO2 of 69 ± 3% and a cFTOE of 23 ± 4. Of the 26 newborns with normal values of regional saturation, 10 showed a decrease of rSO2 to 50 ± 3 with a rFTOE of 45 ± 3 when the hematocrit fell to 20-22%. After a packed red cell transfusion, a progressive rise of the rSO2 to 83.8 ± 9.4 and a decline of the rFTOE to 8.1 ± 3.4 were observed. These changes started at the end of the transfusion and became stable in the following 12-24 hours. An increase of the cSO2 to 82.2 ± 2.9 and a decrease of the cFTOE to 12.2 ± 2.90 were observed after the transfusion and after the progressive normalization of the renal oxygenation as well. On the basis of these results, in our Unit only the newborns with a hematocrit ≤ 30 and clear sinking renal saturation values are transfused. In the light of the reported observations, we recognize to the regional oxygenation monitoring a precise role in the process of personalization of the newborn cares in intensive contexts. Despite the requirement for wider observations, the information drawn by the variations of the regional oxygenation in different pathophysiologic processes can substantially help in the prevention of the organ damage, particularly the brain, that upsets still today the results of the neonatal intensive cares. Proceedings of the 9th International Workshop on Neonatology · Cagliari (Italy) · October 23rd-26th, 2013 · Learned lessons, changing practice and cutting-edge researc