Cambiamenti ambientali indotti dalle variazioni climatiche oloceniche e dall’uomo nell’area dell’abitato antico di Pontecagnano

L’abitato antico di Pontecagnano (vii-iii a.C.), sorge su un alto morfologico di natura travertinosa, che in antico emergeva di pochi metri dal settore NO della pianura alluvionale costiera del Fiume Sele. I lavori per l’ampliamento dell’autostrada A3 SA-RC hanno intercettato livelli archeologici rappresentativi di ampie porzioni del territorio urbano e periurbano dell’abitato antico e messo in evidenza un record archeostratigrafico che va dal Pleistocene Superiore ad oggi. Lo studio geomorfologico ed archeo-tephro-stratigrafico di dettaglio, corredato da analisi paleoambientali, ha consentito di delineare gli aspetti salienti dell’evoluzione del paesaggio e degli ambienti nel corso dell’Olocene. Le modifiche dell’ambiente e del paesaggio sono state prevalentemente indotte da condizionamenti antropici sul sistema idraulico e forestale e sull’organizzazione del territorio soprattutto per il periodo di vita dell’abitato, dove si coglie un importante bonifica. Nei periodi precedenti e successivi alla vita dell’abitato i cambiamenti ambientali sono stati indotti da variazioni climatiche e dai prodotti delle eruzioni dei vulcani napoletani.The ancient settlement of Pontecagnano (7th-3rd centuries B.C.) was built up on the travertine plateau overlooking the Sele river on the NW sector of the alluvial-coastal plain. Motorway construction works brought to light archaeological remains of an ancient urban and suburban settlement. Archaeostratigraphical records dated between the late Pleistocene and today have been elucidated. The geomorphological and archaeo-tephro-stratigraphical study, coupled with palaeoenvironmental analysis, allowed us to outline the evolution of the environment during the Holocene. The environmental changes have been mainly induced by human activities, during the 7th -3rd centuries B.C., by land reclamation. During other periods of the Holocene the environmental changes can be attributed to climatic variations and, secondly, to the impact of the distal products of Neapolitan volcanic eruptions on geomorphic systems

Data Science methodologies for predictive analytics in Smart Cities

The goal of this PhD dissertation is to conduct academic and industrial research on Data Science in a variety of fields. An interdisciplinary approach was required to address today's scientific and societal challenges. A three-year training path applied Data Science to two Smart City application domains: Cultural Heritage (CH) and E-Health, with a focus on machine learning (ML) and knowledge graphs (KG). The first application is on classifying and forecasting visitor flow within a museum. By applying Machine Learning to the CH sector, the study examined a mixed dataset of numerical and categorical values. A framework for data processing and information extraction for clustering visitor behaviors was developed to save time. The dissertation then focuses on two e-health topics: healthcare booking prescriptions and image processing for biosensors. Prescriptions issued by general practitioners were modeled as a KG to help optimize government and local e-health services. This dissertation aimed to identify more patterns in data than a legacy dataset and thus make more accurate predictions. The final biosensor application recognizes point of interests in smartphone photos and uses machine learning algorithms to determine their chemical composition. The tool predicts the amount of a compound based on the liquid sample's luminescence. This dissertation's specific research questions concentrate around one question: how can Data Science help construct Smart Cities? This is addressed through a framework for analyzing people moving indoors, an extension of a legacy SQL database to a Knowledge Graph, and the building of a lab-on-hand proof of concept. All of this is accomplished through the use of a wide range of mathematical and software methods, such as machine learning (clustering and classification), image processing, and KG embedding. Python and R with Grakn, AmpliGraph, OpenCV, and scikit-learn have been utilized as toolkits. Among the most important contributions made by this thesis are: a data processing framework for clustering visitor behavior (CH domain); tools to help CH decision-makers better analyze visitor behavior and data clusters (both are critical aspects in any kind of ML and decision-making tools); a framework for KG data management and analysis; a framework for biosensors recognizes point-of-interests in smartphone images and uses machine learning algorithms to estimate a compound's concentration in a liquid sample

The Role of Artificial Intelligence in Fighting the COVID-19 Pandemic

The first few months of 2020 have profoundly changed the way we live our lives and carry out our daily activities. Although the widespread use of futuristic robotaxis and self-driving commercial vehicles has not yet become a reality, the COVID-19 pandemic has dramatically accelerated the adoption of Artificial Intelligence (AI) in different fields. We have witnessed the equivalent of two years of digital transformation compressed into just a few months. Whether it is in tracing epidemiological peaks or in transacting contactless payments, the impact of these developments has been almost immediate, and a window has opened up on what is to come. Here we analyze and discuss how AI can support us in facing the ongoing pandemic. Despite the numerous and undeniable contributions of AI, clinical trials and human skills are still required. Even if different strategies have been developed in different states worldwide, the fight against the pandemic seems to have found everywhere a valuable ally in AI, a global and open-source tool capable of providing assistance in this health emergency. A careful AI application would enable us to operate within this complex scenario involving healthcare, society and research

A Deep Learning approach for Path Prediction in a Location-based IoT system

Knowing in real-time the position of objects and people, both in indoor and outdoor spaces, allows companies and organizations to improve their processes and offer new kind of services. Nowadays Location-based Services (LBS) generate a significant amount of data thank to the widespread of the Internet of Things; since they have been quickly perceived as a potential source of profit, several companies have started to design and develop a wide range of such services. One of the most challenging research tasks is undoubtedly represented by the analysis of LBS data through Machine Learning algorithms and methodologies in order to infer new knowledge and build-up even more customized services. Cultural Heritage is a domain that can benefit from such studies since it is characterized by a strong interaction between people, cultural items and spaces. Data gathered in a museum on visitor movements and behaviours can constitute the knowledge base to realize an advanced monitoring system able to offer museum stakeholders a complete and real-time snapshot of the museum locations occupancy. Furthermore, exploiting such data through Deep Learning methodologies can lead to the development of a predictive monitoring system able to suggest stakeholders the museum locations occupancy not only in real-time but also in the next future, opening new scenarios in the management of a museum. In this paper, we present and discuss a Deep Learning methodology applied to data coming from a non-invasive Bluetooth IoT monitoring system deployed inside a cultural space. Through the analysis of visitors’ paths, the main goal is to predict the occupancy of the available rooms. Experimental results on real data demonstrate the feasibility of the proposed approach; it can represent a useful instrument, in the hands of the museum management, to enhance the quality-of-service within this kind of spaces

Path prediction in IoT systems through Markov Chain algorithm

In the Data Technology Era, inferring knowledge from data is an ubiquitous and pervasive research topic. Digital Ecosystems based on the Internet of Things (IoT) are generally designed for generating and collecting complex, real-time and (un)structured data. As one of the main component of the Smart City framework, the huge amount of IoT data has to be opportunely processed, also through Machine Learning algorithms in order to discover new knowledge and to improve the quality-of-life of the citizens. In our research work we propose some learning methodologies to analyse and forecast visitors’ paths within a cultural and complex space. Starting from data collected in a museum equipped with a non-invasive monitoring IoT system, we show how it is possible to discover and predict useful information on visitors’ movements and, finally, we present and discuss some useful insights on their behaviours within a real case-of-study

The influence of anxiety and personality factors on comfort and reachability space a correlational study

Although the effects of several personality factors on interpersonal space (i.e. social space within personal comfort area) are well documented, it is not clear whether they also extend to peripersonal space (i.e. reaching space). Indeed, no study has directly compared these spaces in relation to personality and anxiety factors even though such a comparison would help to clarify to what extent they share similar mechanisms and characteristics. The aim of the present paper was to investigate whether personality dimensions and anxiety levels are associated with reaching and comfort distances. Seventy university students (35 females) were administered the Big Five Questionnaire and the State-Trait Anxiety Inventory; afterwards, they had to provide reachability- and comfort-distance judgments towards human confederates while standing still (passive) or walking towards them (active). The correlation analyses showed that both spaces were positively related to anxiety and negatively correlated with the Dynamism in the active condition. Moreover, in the passive condition higher Emotional Stability was related to shorter comfort distance, while higher cognitive Openness was associated with shorter reachability distance. The implications of these results are discussed

Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's next

Physics-Informed Neural Networks (PINN) are neural networks (NNs) that encode model equations, like Partial Differential Equations (PDE), as a component of the neural network itself. PINNs are nowadays used to solve PDEs, fractional equations, integral-differential equations, and stochastic PDEs. This novel methodology has arisen as a multi-task learning framework in which a NN must fit observed data while reducing a PDE residual. This article provides a comprehensive review of the literature on PINNs: while the primary goal of the study was to characterize these networks and their related advantages and disadvantages. The review also attempts to incorporate publications on a broader range of collocation-based physics informed neural networks, which stars form the vanilla PINN, as well as many other variants, such as physics-constrained neural networks (PCNN), variational hp-VPINN, and conservative PINN (CPINN). The study indicates that most research has focused on customizing the PINN through different activation functions, gradient optimization techniques, neural network structures, and loss function structures. Despite the wide range of applications for which PINNs have been used, by demonstrating their ability to be more feasible in some contexts than classical numerical techniques like Finite Element Method (FEM), advancements are still possible, most notably theoretical issues that remain unresolved

The influence of anxiety and personality factors on comfort and reachability space a correlational study

Although the effects of several personality factors on interpersonal space (i.e. social space within personal comfort area) are well documented, it is not clear whether they also extend to peripersonal space (i.e. reaching space). Indeed, no study has directly compared these spaces in relation to personality and anxiety factors even though such a comparison would help to clarify to what extent they share similar mechanisms and characteristics. The aim of the present paper was to investigate whether personality dimensions and anxiety levels are associated with reaching and comfort distances. Seventy university students (35 females) were administered the Big Five Questionnaire and the State-Trait Anxiety Inventory; afterwards, they had to provide reachability- and comfort-distance judgments towards human confederates while standing still (passive) or walking towards them (active). The correlation analyses showed that both spaces were positively related to anxiety and negatively correlated with the Dynamism in the active condition. Moreover, in the passive condition higher Emotional Stability was related to shorter comfort distance, while higher cognitive Openness was associated with shorter reachability distance. The implications of these results are discussed