The mathematical analysis towards the dependence on the initial data for a discrete thermostatted kinetic framework for biological systems composed of interacting entities

This paper is devoted to a mathematical proof of the continuous dependence on the initial data for the discrete thermostatted kinetic framework, for all T > 0. This is a versatile model for describing the time-evolution of a biological complex system which is composed by a large number of interacting entities, called active particles, and is subjected to an external force field due to the environment. A thermostat term is introduced in order to keep the 2nd-order moment of the system, corresponding to the physical global activation energy, constant in time. This model is expressed by a system of nonlinear ordinary differential equations with quadratic nonlinearity

On the Cauchy problem of vectorial thermostatted kinetic frameworks

This paper is devoted to the derivation and mathematical analysis of new thermostatted kinetic theory frameworks for the modeling of nonequilibrium complex systems composed by particles whose microscopic state includes a vectorial state variable. The mathematical analysis refers to the global existence and uniqueness of the solution of the related Cauchy problem. Specifically, the paper is divided in two parts. In the first part the thermostatted framework with a continuous vectorial variable is proposed and analyzed. The framework consists of a system of partial integro-differential equations with quadratic type nonlinearities. In the second part the thermostatted framework with a discrete vectorial variable is investigated. Real world applications, such as social systems and crowd dynamics, and future research directions are outlined in the paper

Energy Assessment of A PCM–Embedded Plaster: Embodied Energy Versus Operational Energy

Phase change materials (PCMs) are an emerging technology that can be integrated in building envelope components. PCMs are able to stabilise indoor air temperature and increase thermal energy storage especially in lightweight constructions. Within a research activity aimed at developing advanced plasters with improved thermal properties, a plaster which incorporates a microencapsulated paraffin-based PCM was developed. The paper highlights the importance of an overall analysis, facing both operational and embodied energy, since the expected decrease of the energy consumption during the operational stage difficultly counterbalances the high energy impact related to manufacturing processes

ePhysio: A Wearables-Enabled Platform for the Remote Management of Musculoskeletal Diseases

Technology advancements in wireless communication and embedded computing are fostering their evolution from standalone elements to smart objects seamlessly integrated in the broader context of the Internet of Things. In this context, wearable sensors represent the building block for new cyber-physical social systems, which aim at improving the well-being of people by monitoring and measuring their activities and provide an immediate feedback to the users. In this paper, we introduce ePhysio, a large-scale and flexible platform for sensor-assisted physiotherapy and remote management of musculoskeletal diseases. The system leverages networking and computing tools to provide real-time and ubiquitous monitoring of patients. We propose three use cases which differ in scale and context and are characterized by different human interactions: single-user therapy, indoor group therapy, and on-field therapy. For each use case, we identify the social interactions, e.g., between the patient and the physician and between different users and the performance requirements in terms of monitoring frequency, communication, and computation. We then propose three related deployments, highlighting the technologies that can be applied in a real system. Finally, we describe a proof-of-concept implementation, which demonstrates the feasibility of the proposed solution

NLG-Metricverse: An End-to-End Library for Evaluating Natural Language Generation

Driven by deep learning breakthroughs, natural language generation (NLG) models have been at the center of steady progress in the last few years, with a ubiquitous task influence. However, since our ability to generate human-indistinguishable artificial text lags behind our capacity to assess it, it is paramount to develop and apply even better automatic evaluation metrics. To facilitate researchers to judge the effectiveness of their models broadly, we introduce NLG-Metricverse—an end-to-end open-source library for NLG evaluation based on Python. Our framework provides a living collection of NLG metrics in a unified and easy-to-use environment, supplying tools to efficiently apply, analyze, compare, and visualize them. This includes (i) the extensive support to heterogeneous automatic metrics with n-arity management, (ii) the meta-evaluation upon individual performance, metric-metric and metric-human correlations, (iii) graphical interpretations for helping humans better gain score intuitions, (iv) formal categorization and convenient documentation to accelerate metrics understanding. NLG-Metricverse aims to increase the comparability and replicability of NLG research, hopefully stimulating new contributions in the area

Moving Auto-Correlation Window Approach for Heart Rate Estimation in Ballistocardiography Extracted by Mattress-Integrated Accelerometers

Continuous heart monitoring is essential for early detection and diagnosis of cardiovascular diseases, which are key factors for the evaluation of health status in the general population. Therefore, in the future, it will be increasingly important to develop unobtrusive and transparent cardiac monitoring technologies for the population. The possible approaches are the development of wearable technologies or the integration of sensors in daily-life objects. We developed a smart bed for monitoring cardiorespiratory functions during the night or in the case of continuous monitoring of bedridden patients. The mattress includes three accelerometers for the estimation of the ballistocardiogram (BCG). BCG signal is generated due to the vibrational activity of the body in response to the cardiac ejection of blood. BCG is a promising technique but is usually replaced by electrocardiogram due to the difficulty involved in detecting and processing the BCG signals. In this work, we describe a new algorithm for heart parameter extraction from the BCG signal, based on a moving auto-correlation sliding-window. We tested our method on a group of volunteers with the simultaneous co-registration of electrocardiogram (ECG) using a single-lead configuration. Comparisons with ECG reference signals indicated that the algorithm performed satisfactorily. The results presented demonstrate that valuable cardiac information can be obtained from the BCG signal extracted by low cost sensors integrated in the mattress. Thus, a continuous unobtrusive heart-monitoring through a smart bed is now feasible

giCASES: Case based learning in the field of Geographical Information

Painho, M., Baptista, A., Oliveira, T. H. M. D., & Carbonaro, M. (2018). giCASES: Case based learning in the field of Geographical Information. Poster session presented at 10th GeoMundus Conference 2018, Lisbon, Portugal.publishersversionpublishe

giCASES: un approccio innovativo all’apprendimento nel settore dell’Informazione Geografica

giCASES – Creation of a University-Enteprise Alliance for a Spatially Enabled Society (http://www.gicases.eu) è un’Alleanza per la Conoscenza co-finanziata dal Programma UE ERASMUS+ che mira a facilitare la creazione collaborativa, la gestione e la condivisione di conoscenze nel campo dell’Informazione Geografica (GI), nonché ad agevolare e rafforzare l’innovazione nell’istruzione e nell’industria in tema di GI. Questi obiettivi vengono perseguiti attraverso lo sviluppo di approcci innovativi e multidisciplinari all’insegnamento e apprendimento nel settore GI e facilitando lo scambio, il flusso e la co-creazione di conoscenza. L’approccio consiste nello sviluppo collaborativo e condiviso, tra imprese e università, di nuovi materiali e processi di apprendimento basati su casi reali. Uno degli obiettivi del progetto è infatti quello di sviluppare una piattaforma collaborativa per la creazione e condivisione di risorse, che sarà la chiave per lo sviluppo di conoscenze condivise e per la messa in opera dei casi di studio. La piattaforma sarà basata su tecnologie esistenti, come Learning Management Systems (piattaforme e-Learning), strumenti per i Massive Open Online Courses (MOOC), strumenti di Project Management e diversi software GIS, con una preferenza per le soluzioni Open Source. I risultati del progetto (materiale di apprendimento e processi) saranno resi disponibili con licenza aperta e centralizzati sulla piattaforma, aperta ad altre comunità e portatori di interesse. Il progetto, della durata di 3 anni, vede la partecipazione di 14 partner da 8 diversi paesi europei, con una componente bilanciata di università e imprese (si rimanda al sito di progetto per i dettagli sui partner coinvolti). Il materiale e l’approccio sviluppati verranno sottoposti ad un’accurata fase di test e validazione per garantirne la ri-usabilità da parte di altri portatori di interesse. L’approccio viene declinato su 7 casi di studio (CS) già identificati per testare la metodologia di apprendimento: CS1 - Use of indoor GIS in healthcare; CS2 - Environmental analysis using cloud service system; CS3 - From INSPIRE to e-Government; CS4 - Integrated management of the underground; CS5 - Harmonizing data flows in Energy saving EU policies; CS6 - Forest management; CS7 - Harmonized data and services in forest fire management. Per ognuno di essi, viene definito un dettagliato piano di lavoro comprendente la descrizione degli attori coinvolti, il contesto applicativo, la tempistica di svolgimento e i risultati attesi. Sulla base dei risultati del test, gli strumenti per la collaborazione e il materiale di apprendimento potranno essere rimodulati in vista di una successiva fruizione da parte degli utenti interessati. Nei 7 casi di studio è previsto l’utilizzo di dati aperti (su tutti OpenStreetMap), standard e servizi OGC e una vasta gamma di tecnologie FOSS4G tra cui QGIS, GRASS GIS, GeoServer, PostGIS e Geomajas

Development of Vegetal Based Thermal Plasters with Low Environmental Impact: Optimization Process through an Integrated Approach

The use of thermal insulating plasters represents an effective solution in energy retrofit of existing buildings. Thermal properties are usually improved through the addition on the plaster formulation of Light Weight Aggregates, as expanded polystyrene and perlite. The drawback of these thermal plasters is the higher environmental impact, especially when added to natural binders, as natural hydraulic lime. Within a research activity a process of optimization was followed in order to get the most effective blend, applying iteratively the LCA methodology, measuring the thermal conductivity and testing the environmental impact in terms of Volatile Organic Compounds and formaldehyde emission rates