Enabling Human Centric Smart Campuses via Edge Computing and Connected Objects

Early definitions of Smart Building focused almost entirely on the technology aspect and did not suggest user interaction at all. Indeed, today we would attribute it more to the concept of the automated building. In this sense, control of comfort conditions inside buildings is a problem that is being well investigated, since it has a direct effect on users’ productivity and an indirect effect on energy saving. Therefore, from the users’ perspective, a typical environment can be considered comfortable, if it’s capable of providing adequate thermal comfort, visual comfort and indoor air quality conditions and acoustic comfort. In the last years, the scientific community has dealt with many challenges, especially from a technological point of view. For instance, smart sensing devices, the internet, and communication technologies have enabled a new paradigm called Edge computing that brings computation and data storage closer to the location where it is needed, to improve response times and save bandwidth. This has allowed us to improve services, sustainability and decision making. Many solutions have been implemented such as smart classrooms, controlling the thermal condition of the building, monitoring HVAC data for energy-efficient of the campus and so forth. Though these projects provide to the realization of smart campus, a framework for smart campus is yet to be determined. These new technologies have also introduced new research challenges: within this thesis work, some of the principal open challenges will be faced, proposing a new conceptual framework, technologies and tools to move forward the actual implementation of smart campuses. Keeping in mind, several problems known in the literature have been investigated: the occupancy detection, noise monitoring for acoustic comfort, context awareness inside the building, wayfinding indoor, strategic deployment for air quality and books preserving.

Applied deep learning and data science with a human-centric and data-centric approach

The term Artificial intelligence acquired a lot of baggage since its introduction and in its current incarnation is synonymous with Deep Learning. The sudden availability of data and computing resources has opened the gates to myriads of applications. Not all are created equal though, and problems might arise especially for fields not closely related to the tasks that pertain tech companies that spearheaded DL. The perspective of practitioners seems to be changing, however. Human-Centric AI emerged in the last few years as a new way of thinking DL and AI applications from the ground up, with a special attention at their relationship with humans. The goal is designing a system that can gracefully integrate in already established workflows, as in many real-world scenarios AI may not be good enough to completely replace its humans. Often this replacement may even be unneeded or undesirable. Another important perspective comes from, Andrew Ng, a DL pioneer, who recently started shifting the focus of development from “better models” towards better, and smaller, data. He defined his approach Data-Centric AI. Without downplaying the importance of pushing the state of the art in DL, we must recognize that if the goal is creating a tool for humans to use, more raw performance may not align with more utility for the final user. A Human-Centric approach is compatible with a Data-Centric one, and we find that the two overlap nicely when human expertise is used as the driving force behind data quality. This thesis documents a series of case-studies where these approaches were employed, to different extents, to guide the design and implementation of intelligent systems. We found human expertise proved crucial in improving datasets and models. The last chapter includes a slight deviation, with studies on the pandemic, still preserving the human and data centric perspective.

Collaborative governance of smart cities: addressing multiple relations of actors involved

This dissertation aims to contribute to the discourse on the governance of smart cities (SC) by examining the collaborative relationships between various actors involved in developing and implementing SC initiatives. Poorly organized collaboration can lead to conflicts and misunderstandings, resulting in failures in realizing such complex technological initiatives. Hence, capturing the main elements of SC collaboration becomes essential for understanding how they should be developed and managed. However, the topic has been limitedly explored in prior research, with fragmented studies on narrow aspects related to the SC governance. Using Russia as an empirical setting, the study focuses on the interplay of both government and non-governmental stakeholders in constructing collaborative relationships within SC, covering both vertical and horizontal dimensions of their interaction. The overarching goal of this research is to understand how collaborative governance unfolds in the SC context by stating two guiding research questions: 1) who are the dominant actors in SC and what are their roles? 2) what are the relationships forged among them? The dissertation investigates the SC initiatives across three different cities – Moscow, Saint Petersburg, and Perm – in a format of empirical illustration as well as an in-depth case study. The dissertation provides three main contributions. First, it strengthens the link between the SC domain, public governance, and literature on cross-sectoral collaboration by highlighting ‘urban smartness’ as a source for generating multiple values. Second, the thesis offers novel view on the strategic development paths which conceptually shape the SC framework. It connects the techno-centric and human-centric perspectives of SC by showing that they are naturally linked, rather than mutually exclusive. Third, the study illustrates that SC initiatives are contextually dependent, and this dependence covers specificities of public governance, including underlying informal mechanisms, which influence the inception, development, and management of SC in the organizational realms.

On the Democratization of AI: Bridging the Gap with AutoML

Riding the wave of recent groundbreaking achievements, artificial intelligence (AI) is currently the buzzword on everybody’s lips and, allowing algorithms to learn from historical data, Machine Learning (ML) emerged as its pinnacle. The multitude of algorithms, each with unique strengths and weaknesses, highlights the absence of a universal solution and poses a challenging optimization problem. In response, automated machine learning (AutoML) navigates vast search spaces within minimal time constraints. By lowering entry barriers, AutoML emerged as promising the democratization of AI, yet facing some challenges. In data-centric AI, the discipline of systematically engineering data used to build an AI system, the challenge of configuring data pipelines is rather simple. We devise a methodology for building effective data pre-processing pipelines in supervised learning as well as a data-centric AutoML solution for unsupervised learning. In human-centric AI, many current AutoML tools were not built around the user but rather around algorithmic ideas, raising ethical and social bias concerns. We contribute by deploying AutoML tools aiming at complementing, instead of replacing, human intelligence. In particular, we provide solutions for single-objective and multi-objective optimization and showcase the challenges and potential of novel interfaces featuring large language models. Finally, there are application areas that rely on numerical simulators, often related to earth observations, they tend to be particularly high-impact and address important challenges such as climate change and crop life cycles. We commit to coupling these physical simulators with (Auto)ML solutions towards a physics-aware AI. Specifically, in precision farming, we design a smart irrigation platform that: allows real-time monitoring of soil moisture, predicts future moisture values, and estimates water demand to schedule the irrigation.

Edge and Big Data technologies for Industry 4.0 to create an integrated pre-sale and after-sale environment

The fourth industrial revolution, also known as Industry 4.0, has rapidly gained traction in businesses across Europe and the world, becoming a central theme in small, medium, and large enterprises alike. This new paradigm shifts the focus from locally-based and barely automated firms to a globally interconnected industrial sector, stimulating economic growth and productivity, and supporting the upskilling and reskilling of employees. However, despite the maturity and scalability of information and cloud technologies, the support systems already present in the machine field are often outdated and lack the necessary security, access control, and advanced communication capabilities. This dissertation proposes architectures and technologies designed to bridge the gap between Operational and Information Technology, in a manner that is non-disruptive, efficient, and scalable. The proposal presents cloud-enabled data-gathering architectures that make use of the newest IT and networking technologies to achieve the desired quality of service and non-functional properties. By harnessing industrial and business data, processes can be optimized even before product sale, while the integrated environment enhances data exchange for post-sale support. The architectures have been tested and have shown encouraging performance results, providing a promising solution for companies looking to embrace Industry 4.0, enhance their operational capabilities, and prepare themselves for the upcoming fifth human-centric revolution.

Investigating the role of single point mutations in the human proteome: a computational study

In the post genomic era with the massive production of biological data the understanding of factors affecting protein stability is one of the most important and challenging tasks for highlighting the role of mutations in relation to human maladies. The problem is at the basis of what is referred to as molecular medicine with the underlying idea that pathologies can be detailed at a molecular level. To this purpose scientific efforts focus on characterising mutations that hamper protein functions and by these affect biological processes at the basis of cell physiology. New techniques have been developed with the aim of detailing single nucleotide polymorphisms (SNPs) at large in all the human chromosomes and by this information in specific databases are exponentially increasing. Eventually mutations that can be found at the DNA level, when occurring in transcribed regions may then lead to mutated proteins and this can be a serious medical problem, largely affecting the phenotype. Bioinformatics tools are urgently needed to cope with the flood of genomic data stored in database and in order to analyse the role of SNPs at the protein level. In principle several experimental and theoretical observations are suggesting that protein stability in the solvent-protein space is responsible of the correct protein functioning. Then mutations that are found disease related during DNA analysis are often assumed to perturb protein stability as well. However so far no extensive analysis at the proteome level has investigated whether this is the case. Also computationally methods have been developed to infer whether a mutation is disease related and independently whether it affects protein stability. Therefore whether the perturbation of protein stability is related to what it is routinely referred to as a disease is still a big question mark. In this work we have tried for the first time to explore the relation among mutations at the protein level and their relevance to diseases with a large-scale computational study of the data from different databases. To this aim in the first part of the thesis for each mutation type we have derived two probabilistic indices (for 141 out of 150 possible SNPs): the perturbing index (Pp), which indicates the probability that a given mutation effects protein stability considering all the “in vitro” thermodynamic data available and the disease index (Pd), which indicates the probability of a mutation to be disease related, given all the mutations that have been clinically associated so far. We find with a robust statistics that the two indexes correlate with the exception of all the mutations that are somatic cancer related. By this each mutation of the 150 can be coded by two values that allow a direct comparison with data base information. Furthermore we also implement computational methods that starting from the protein structure is suited to predict the effect of a mutation on protein stability and find that overpasses a set of other predictors performing the same task. The predictor is based on support vector machines and takes as input protein tertiary structures. We show that the predicted data well correlate with the data from the databases. All our efforts therefore add to the SNP annotation process and more importantly found the relationship among protein stability perturbation and the human variome leading to the diseasome.

Wireless Networks for Body-Centric Communications

Progress in miniaturization of electronic components and design of wireless systems paved the way towards ubiquitous and pervasive communications, enabling anywhere and anytime connectivity. Wireless devices present on, inside, around the human body are becoming commonly used, leading to the class of body-centric communications. The presence of the body with all its peculiar characteristics has to be properly taken into account in the development and design of wireless networks in this context. This thesis addresses various aspects of body-centric communications, with the aim of investigating network performance achievable in different scenarios. The main original contributions pertain to the performance evaluation for Wireless Body Area Networks (WBANs) at the Medium Access Control layer: the application of Link Adaptation to these networks is proposed, Carrier Sense Multiple Access with Collision Avoidance algorithms used for WBAN are extensively investigated, coexistence with other wireless systems is examined. Then, an analytical model for interference in wireless access network is developed, which can be applied to the study of communication between devices located on humans and fixed nodes of an external infrastructure. Finally, results on experimental activities regarding the investigation of human mobility and sociality are presented.

From Radio Channel Modeling to a System Level Perspective in Body-Centric Communications

Body-centric communications are emerging as a new paradigm in the panorama of personal communications. Being concerned with human behaviour, they are suitable for a wide variety of applications. The advances in the miniaturization of portable devices to be placed on or around the body, foster the diffusion of these systems, where the human body is the key element defining communication characteristics. This thesis investigates the human impact on body-centric communications under its distinctive aspects. First of all, the unique propagation environment defined by the body is described through a scenario-based channel modeling approach, according to the communication scenario considered, i.e., on- or on- to off-body. The novelty introduced pertains to the description of radio channel features accounting for multiple sources of variability at the same time. Secondly, the importance of a proper channel characterisation is shown integrating the on-body channel model in a system level simulator, allowing a more realistic comparison of different Physical and Medium Access Control layer solutions. Finally, the structure of a comprehensive simulation framework for system performance evaluation is proposed. It aims at merging in one tool, mobility and social features typical of the human being, together with the propagation aspects, in a scenario where multiple users interact sharing space and resources.

Lo sviluppo di disturbi psichiatrici in pazienti hcv+ sottoposti a terapia antivirale: L'impatto dei nuovi farmaci

Introduzione:l’interferone (IFN) usato per l’eradicazione del virus dell’Epatite C, induce effetti collaterali anche riferibili alla sfera psichica. I dati sugli eventi avversi di tipo psichiatrico dei nuovi farmaci antivirali (DAA) sono limitati. Lo scopo di questo studio è di valutare lo sviluppo di effetti collaterali di tipo psichiatrico in corso di due distinti schemi di trattamento: IFN-peghilato e ribavirina [terapia duplice (standard o SOC)]; DAA in associazione a IFN-peghilato e ribavirina (terapia triplice). Metodi: pazienti HCV+ consecutivi seguiti presso l’Ambulatorio delle Epatiti Croniche della Semeiotica Medica del Dipartimento di Scienze Mediche e Chirurgiche dell’Università di Bologna in procinto di intraprendere un trattamento antivirale a base di IFN, sottoposti ad esame psicodiagnostico composto da intervista clinica semistrutturata e test autosomministrati: BDI, STAXI-2, Hamilton Anxiety Scale, MMPI – 2. Risultati: Sono stati arruolati 84 pazienti, 57/84 (67.9%) nel gruppo in triplice e 27/84 nel gruppo SOC. Quasi tutti i pazienti arruolati hanno eseguito l’intervista clinica iniziale (82/84; 97.6%), mentre scarsa è stata l’aderenza ai test (valori missing>50%). Ad eccezione dell’ansia, la prevalenza di tutti gli altri disturbi (irritabilità, astenia, disfunzioni neurocognitive, dissonnia) aumentava in corso di trattamento. In corso di terapia antivirale 43/84 (51.2%) hanno avuto bisogno di usufruire del servizio di consulenza psichiatrica e 48/84 (57.1%) hanno ricevuto una psicofarmacoterapia di supporto, senza differenze significative fra i due gruppi di trattamento. Conclusioni : uno degli elementi più salienti dello studio è stata la scarsa aderenza ai test psicodiagnostici, nonostante l’elevata prevalenza di sintomi psichiatrici. I risultati di questo studio oltre ad evidenziare l’importanza dei sintomi psichiatrici in corso di trattamento e la rilevanza della consulenza psicologica e psichiatrica per consentire di portare a termine il ciclo terapeutico previsto (migliorandone l’efficacia), ha anche dimostrato che occorre ripensare gli strumenti diagnostici adattandoli probabilmente a questo specifico target.

Evolutionary epigenetics of modern human populations

Epigenetic variability is a new mechanism for the study of human microevolution, because it creates both phenotypic diversity within an individual and within population. This mechanism constitutes an important reservoir for adaptation in response to new stimuli and recent studies have demonstrated that selective pressures shape not only the genetic code but also DNA methylation profiles. The aim of this thesis is the study of the role of DNA methylation changes in human adaptive processes, considering the Italian peninsula and macro-geographical areas. A whole-genome analysis of DNA methylation profile across the Italian penisula identified some genes whose methylation levels differ between individuals of different Italian districts (South, Centre and North of Italy). These genes are involved in nitrogen compound metabolism and genes involved in pathogens response. Considering individuals with different macro-geographical origins (individuals of Asians, European and African ancestry) more significant DMRs (differentially methylated regions) were identified and are located in genes involved in glucoronidation, in immune response as well as in cell comunication processes. A "profile" of each ancestry (African, Asian and European) was described. Moreover a deepen analysis of three candidate genes (KRTCAP3, MAD1L and BRSK2) in a cohort of individuals of different countries (Morocco, Nigeria, China and Philippines) living in Bologna, was performed in order to explore genetic and epigenetic diversity. Moreover this thesis have paved the way for the application of DNA methylation for the study of hystorical remains and in particular for the age-estimation of individuals starting from biological samples (such as teeth or blood). Noteworthy, a mathematical model that considered methylation values of DNA extracted from cementum and pulp of living individuals can estimate chronological age with high accuracy (median absolute difference between age estimated from DNA methylation and chronological age was 1.2 years).

Contaminants of emerging concern in drinking water: assessment of the effectiveness of removal by drinking water treatments and possible risks to human health

Contaminants of emerging concern are increasingly detected in the water cycle, with endocrine-disrupting chemicals (EDCs) receiving attention due to their potential to cause adverse health effects even at low concentrations. Although the EU has recently introduced some EDCs into drinking water legislation, most drinking water treatment plants (DWTPs) are not designed to remove EDCs, making their detection and removal in DWTPs an important challenge. The aim of this doctoral project was to investigate hormones and phenolic compounds as suspected EDCs in drinking waters across the Romagna area (Italy). The main objectives were to assess the occurrence of considered contaminants in source and drinking water from three DWTPs, characterize the effectiveness of removal by different water treatment processes, and evaluate the potential biological impact on drinking water and human health. Specifically, a complementary approach of target chemical analysis and effect-based methods was adopted to explore drinking water quality, treatment efficacy, and biological potential. This study found that nonylphenol (NP) was prevalent in all samples, followed by BPA. Sporadic contamination of hormones was found only in source waters. Although the measured EDC concentrations in drinking water did not exceed threshold guideline values, the potential role of DWTPs as an additional source of EDC contamination should be considered. Significant increases in BPA and NP levels were observed during water treatment steps, which were also reflected in estrogenic and mutagenic responses in water samples after the ultrafiltration. This highlights the need to monitor water quality during various treatment processes to improve the efficiency of DWTPs. Biological assessments on finished water did not reveal any bioactivity, except for few treated water samples that exhibited estrogenic responses. Overall, the data emphasize the high quality of produced drinking water and the value of applying integrated chemical analysis and in vitro bioassays for water quality assessment.