Novel investigation methods in Computational Social Dynamics and Complex Systems

In this thesis the evolution of the techno-social systems analysis methods will be reported, through the explanation of the various research experience directly faced. The first case presented is a research based on data mining of a dataset of words association named Human Brain Cloud: validation will be faced and, also through a non-trivial modeling, a better understanding of language properties will be presented. Then, a real complex system experiment will be introduced: the WideNoise experiment in the context of the EveryAware european project. The project and the experiment course will be illustrated and data analysis will be displayed. Then the Experimental Tribe platform for social computation will be introduced . It has been conceived to help researchers in the implementation of web experiments, and aims also to catalyze the cumulative growth of experimental methodologies and the standardization of tools cited above. In the last part, three other research experience which already took place on the Experimental Tribe platform will be discussed in detail, from the design of the experiment to the analysis of the results and, eventually, to the modeling of the systems involved. The experiments are: CityRace, about the measurement of human traffic-facing strategies; laPENSOcosì, aiming to unveil the political opinion structure; AirProbe, implemented again in the EveryAware project framework, which consisted in monitoring air quality opinion shift of a community informed about local air pollution. At the end, the evolution of the technosocial systems investigation methods shall emerge together with the opportunities and the threats offered by this new scientific path.

Original analytical methods for the determination of psychoactive compounds in complex matrices

This thesis work aims to develop original analytical methods for the determination of drugs with a potential for abuse, for the analysis of substances used in the pharmacological treatment of drug addiction in biological samples and for the monitoring of potentially toxic compounds added to street drugs. In fact reliable analytical techniques can play an important role in this setting. They can be employed to reveal drug intake, allowing the identification of drug users and to assess drug blood levels, assisting physicians in the management of the treatment. Pharmacological therapy needs to be carefully monitored indeed in order to optimize the dose scheduling according to the specific needs of the patient and to discourage improper use of the medication. In particular, different methods have been developed for the detection of gamma-hydroxybutiric acid (GHB), prescribed for the treatment of alcohol addiction, of glucocorticoids, one of the most abused pharmaceutical class to enhance sport performance and of adulterants, pharmacologically active compounds added to illicit drugs for recreational purposes. All the presented methods are based on capillary electrophoresis (CE) and high performance liquid chromatography (HPLC) coupled to various detectors (diode array detector, mass spectrometer). Biological samples pre-treatment was carried out using different extraction techniques, liquid-liquid extraction (LLE) and solid phase extraction (SPE). Different matrices have been considered: human plasma, dried blood spots, human urine, simulated street drugs. These developed analytical methods are individually described and discussed in this thesis work.

Impact of Protein Induced by Vitamin K Absence (PIVKA-II) in diagnosis and monitoring of patients with hepatocellular carcinoma

Background and Aims: Hepatocellular carcinoma (HCC) represents the second leading cause of cancer deaths worldwide. Protein induced by vitamin K absence (PIVKA-II) has been proposed as potential screening biomarker for HCC.This study has been designed to evaluate the role of PIVKA-II as diagnostic HCC marker, through the comparison between PIVKA-II and alpha-fetoprotein (AFP) serum levels on HCC patients and the two control groupsof patients with liver disease and without HCC. Methods: In an Italian prospective cohort, PIVKA-II levels were assessed on serum samplesby an automated chemiluminescent immunoassay (Abbott ARCHITECT). The study population included 65 patients with HCC (both “de novo” and recurrent), 111 with liver cirrhosis (LC) and 111 with chronic hepatitis C (CHC). Results: PIVKA-II levels were increased in patients with HCC (median 63.75, range: 12-2675 mAU/mL) compared to LC (median value: 30.95, range: 11.70–1251mAU / mL, Mann Whitney test p < 0.0001) and CHC (median value: 24.89, range: 12.98-67.68mAU / mL, p < 0.0001).The area under curve (AUC) for PIVKA-II was 0.817 (95% Confidence Interval(CI), 0.752-0.881). At the optimal threshold of 37 mAU / mL, identified by the Youden Index, the sensitivity and specificity were 79% and 76%, respectively. PIVKA-II was a better biomarker than AFP for the diagnosis of HCC, since the AUC for AFP was 0.670 (95% CI 0.585-0.754, p<0.0001) and at the best cutoff of 16.4 ng / mL AFP yielded 98% specificity but only 34% sensitivity. Conclusions:These initial data suggest the potential utility of this tool in the diagnosis of HCC.PIVKA-II alone or in combination may help to an early diagnosis of HCC and a significant optimization of patient management.

mHealth-based Methods for Neuromotor Assessment and Rehabilitation

Recent years observed massive growth in wearable technology, everything can be smart: phones, watches, glasses, shirts, etc. These technologies are prevalent in various fields: from wellness/sports/fitness to the healthcare domain. The spread of this phenomenon led the World-Health-Organization to define the term 'mHealth' as "medical and public health practice supported by mobile devices, such as mobile phones, patient monitoring devices, personal digital assistants, and other wireless devices". Furthermore, mHealth solutions are suitable to perform real-time wearable Biofeedback (BF) systems: sensors in the body area network connected to a processing unit (smartphone) and a feedback device (loudspeaker) to measure human functions and return them to the user as (bio)feedback signal. During the COVID-19 pandemic, this transformation of the healthcare system has been dramatically accelerated by new clinical demands, including the need to prevent hospital surges and to assure continuity of clinical care services, allowing pervasive healthcare. Never as of today, we can say that the integration of mHealth technologies will be the basis of this new era of clinical practice. In this scenario, this PhD thesis's primary goal is to investigate new and innovative mHealth solutions for the Assessment and Rehabilitation of different neuromotor functions and diseases. For the clinical assessment, there is the need to overcome the limitations of subjective clinical scales. Creating new pervasive and self-administrable mHealth solutions, this thesis investigates the possibility of employing innovative systems for objective clinical evaluation. For rehabilitation, we explored the clinical feasibility and effectiveness of mHealth systems. In particular, we developed innovative mHealth solutions with BF capability to allow tailored rehabilitation. The main goal that a mHealth-system should have is improving the person's quality of life, increasing or maintaining his autonomy and independence. To this end, inclusive design principles might be crucial, next to the technical and technological ones, to improve mHealth-systems usability.

Exploiting the environment for structural health monitoring: Strategies, algorithms, and technologies

A densely built environment is a complex system of infrastructure, nature, and people closely interconnected and interacting. Vehicles, public transport, weather action, and sports activities constitute a manifold set of excitation and degradation sources for civil structures. In this context, operators should consider different factors in a holistic approach for assessing the structural health state. Vibration-based structural health monitoring (SHM) has demonstrated great potential as a decision-supporting tool to schedule maintenance interventions. However, most excitation sources are considered an issue for practical SHM applications since traditional methods are typically based on strict assumptions on input stationarity. Last-generation low-cost sensors present limitations related to a modest sensitivity and high noise floor compared to traditional instrumentation. If these devices are used for SHM in urban scenarios, short vibration recordings collected during high-intensity events and vehicle passage may be the only available datasets with a sufficient signal-to-noise ratio. While researchers have spent efforts to mitigate the effects of short-term phenomena in vibration-based SHM, the ultimate goal of this thesis is to exploit them and obtain valuable information on the structural health state. First, this thesis proposes strategies and algorithms for smart sensors operating individually or in a distributed computing framework to identify damage-sensitive features based on instantaneous modal parameters and influence lines. Ordinary traffic and people activities become essential sources of excitation, while human-powered vehicles, instrumented with smartphones, take the role of roving sensors in crowdsourced monitoring strategies. The technical and computational apparatus is optimized using in-memory computing technologies. Moreover, identifying additional local features can be particularly useful to support the damage assessment of complex structures. Thereby, smart coatings are studied to enable the self-sensing properties of ordinary structural elements. In this context, a machine-learning-aided tomography method is proposed to interpret the data provided by a nanocomposite paint interrogated electrically.

Monitoring of Mediterranean marine benthic biodiversity through novel, standardized, and integrated approaches

Marine healthy ecosystems support life on Earth and human well-being thanks to their biodiversity, which is proven to decline mainly due to anthropogenic stressors. Monitoring how marine biodiversity changes trough space and time is needed to properly define and enroll effective actions towards habitat conservation and preservation. This is particularly needed in those areas that are very rich in species compared to their low surface extension and are characterized by strong anthropic pressures, such as the Mediterranean Sea. Subtidal rocky benthic Mediterranean habitats have a complex structural architecture, hosting a panoply of tiny organisms (cryptofauna) that inhabit crevices and caves, but that are still unknown. Different artificial standardized sampling structures (SSS) and methods have been developed and employed to characterize the cryptofauna, allowing for data replicability and comparability across regions. Organisms growing on these artificial structures can be identified coupling morphological taxonomy and DNA barcoding and metabarcoding. The metabarcoding allows for the identification of organisms in a bulk sample without morphological analysis, and it is based on comparing the genetic similarities of the assessed organisms with barcoding sequences present in online barcoding repositories. Nevertheless, barcoded species nowadays represent only a small portion of known species, and barcoding reference databases are not always curated and updated on a regular basis. In this Thesis I used an integrative approach to characterize benthic marine biodiversity, specifically coupling morphological and molecular techniques with the employment of SSS. Moreover, I upgraded the actual status of COI (cytochrome c oxidase subunit I) barcoding of marine metazoans, and I built a customized COI barcoding reference database for metabarcoding studies on temperate biogenic reefs. This work implemented the knowledge about diversity of Mediterranean marine communities, laying the groundworks for monitoring marine and environmental changes that will occur in the next future as consequences of anthropic and climate threats.

Monitoring of New Psychoactive Substances in the hair of patients with a history of addiction. Evaluation of the public health impact.

Introduction. The term New Psychoactive Substances (NPS) encompasses a broad category of drugs which have become available on the market in recent years and whose illicit use for recreational purposes has recently exploded. The analysis of NPS usually requires mass spectrometry based techniques. The aim of our study was to define the preva-lence of NPS consumption in patients with a history of drug addiction followed by Public Services for Pathological Addictions, with the purpose of highlighting the effective presence of NPS within the area of Bologna and evaluating their association with classical drugs of abuse (DOA). Materials and methods. Sustained by literature, a multi-analyte UHPLC-MS/MS method for the identification of 127 NPS (phenethylamines, arylcyclohexylamines, synthetic opioids, tryptamines, synthetic cannabinoids, synthetic cathinones, designer benzodiazepines) and 15 classic drugs of abuse (DOA) in hair samples was developed and validated according to International Guidelines [112]. Samples pretreatment consisted of washing steps and overnight incubation at 45°C in an acid mixture of methanol and water. After cooling, supernatant were injected into the chromatographic system coupled with a tandem mass spectrometry detector. Results. Successful validation was achieved for almost all of the compounds. The method met all the required technical parameters. LOQ was set from 4 to 80 pg/mg The developed method was applied to 107 cases (85 males and 22 females) of clinical interest. Out of 85 hair samples resulting positive to classical drugs of abuse, NPS were found in twelve (8 male and 4 female). Conclusion. The present methodology represents an easy, low cost, wide-panel method for the de-tection of 127 NPS and 15 DOA in hair samples. Such multi-analyte methods facilitates the study of the prevalence of drugs abused that will enable the competent control authorities to obtain evi-dence-based reports regarding the critical spread of the threat represented by NPS.

Monitoring time-dependent deformation patterns through geodetic techniques

In this thesis we focus on the analysis and interpretation of time dependent deformations recorded through different geodetic methods. Firstly, we apply a variational Bayesian Independent Component Analysis (vbICA) technique to GPS daily displacement solutions, to separate the postseismic deformation that followed the mainshocks of the 2016-2017 Central Italy seismic sequence from the other, hydrological, deformation sources. By interpreting the signal associated with the postseismic relaxation, we model an afterslip distribution on the faults involved by the mainshocks consistent with the co-seismic models available in literature. We find evidences of aseismic slip on the Paganica fault, responsible for the Mw 6.1 2009 L’Aquila earthquake, highlighting the importance of aseismic slip and static stress transfer to properly model the recurrence of earthquakes on nearby fault segments. We infer a possible viscoelastic relaxation of the lower crust as a contributing mechanism to the postseismic displacements. We highlight the importance of a proper separation of the hydrological signals for an accurate assessment of the tectonic processes, especially in cases of mm-scale deformations. Contextually, we provide a physical explanation to the ICs associated with the observed hydrological processes. In the second part of the thesis, we focus on strain data from Gladwin Tensor Strainmeters, working on the instruments deployed in Taiwan. We develop a novel approach, completely data driven, to calibrate these strainmeters. We carry out a joint analysis of geodetic (strainmeters, GPS and GRACE products) and hydrological (rain gauges and piezometers) data sets, to characterize the hydrological signals in Southern Taiwan. Lastly, we apply the calibration approach here proposed to the strainmeters recently installed in Central Italy. We provide, as an example, the detection of a storm that hit the Umbria-Marche regions (Italy), demonstrating the potential of strainmeters in following the dynamics of deformation processes with limited spatio-temporal signature

Modelling, simulations and monitoring for sustainable plant and animal production systems through precision technique

Protected crop production is a modern and innovative approach to cultivating plants in a controlled environment to optimize growth, yield, and quality. This method involves using structures such as greenhouses or tunnels to create a sheltered environment. These productive solutions are characterized by a careful regulation of variables like temperature, humidity, light, and ventilation, which collectively contribute to creating an optimal microclimate for plant growth. Heating, cooling, and ventilation systems are used to maintain optimal conditions for plant growth, regardless of external weather fluctuations. Protected crop production plays a crucial role in addressing challenges posed by climate variability, population growth, and food security. Similarly, animal husbandry involves providing adequate nutrition, housing, medical care and environmental conditions to ensure animal welfare. Then, sustainability is a critical consideration in all forms of agriculture, including protected crop and animal production. Sustainability in animal production refers to the practice of producing animal products in a way that minimizes negative impacts on the environment, promotes animal welfare, and ensures the long-term viability of the industry. Then, the research activities performed during the PhD can be inserted exactly in the field of Precision Agriculture and Livestock farming. Here the focus is on the computational fluid dynamic (CFD) approach and environmental assessment applied to improve yield, resource efficiency, environmental sustainability, and cost savings. It represents a significant shift from traditional farming methods to a more technology-driven, data-driven, and environmentally conscious approach to crop and animal production. On one side, CFD is powerful and precise techniques of computer modeling and simulation of airflows and thermo-hygrometric parameters, that has been applied to optimize the growth environment of crops and the efficiency of ventilation in pig barns. On the other side, the sustainability aspect has been investigated and researched in terms of Life Cycle Assessment analyses.