The social components of innovation: from data analysis to mathematical modelling

Novelties are a key driver of societal progress, yet we lack a comprehensive understanding of the factors that generate them. Recent evidence suggests that innovation emerges from the balance between exploiting past discoveries and exploring new possibilities, the so-called ``adjacent possible". This thesis aims at developing new analysis tools and models to study how people navigate the seemingly infinite space of possibilities. Firstly, I extend the notion of the adjacent possible to account for novelties as combinations of existing elements. In particular, I model innovation as a random walk on an expanding complex network of content, in which novelties correspond not only to the first visit of nodes, but also of links. The model correctly reproduces how novelties emerge in empirical data, highlighting the importance of the exploration process in shaping the growth of the network. Secondly, since people continuously interact and exchange information with each other, I investigate the role of social interactions in enhancing discoveries. I hence propose a model where multiple agents extend their adjacent possible through the links of a complex social network, exploiting in this way opportunities coming from their contacts. By adding a social dimension to the adjacent possible, I prove that the discovery potential of an individual is influenced by its position on the social network. Finally, I combine the two concepts of the adjacent possible in the content and social dimension to develop a data-driven model of music exploration on online platforms. In such a model, multiple agents grow their individual space of possibilities by exploring a network of similarity between artists, while exploiting suggestions from their friends on the social network. The comparison with the empirical data indicates that the adjacent possible, in both the content and the social space, plays a crucial role in determining the individual propensity to innovate

Preliminary realization of an electric-powered hydraulic pump system for a waste compactor truck and a techno-economic analysis

Most industrial trucks are equipped with hydraulic systems designed for specic operations, for which the required power is supplied by the internal combustion engine (ICE). The largest share of the power consumption is required by the hydraulic system during idling operations, and, consequently, the current literature focuses on energy saving strategies for the hydraulic system rather than making the vehicle traction more efficient. This study presents the preliminary realization of an electric-powered hydraulic pump system (e-HPS) that drives the lifting of the dumpster and the garbage compaction in a waste compactor truck, rather than traditional ICE-driven hydraulic pump systems (ICE-HPSs). The different components of the e-HPS are described and the battery pack was modelled using the kinetic battery model. The end-of-life of the battery pack was determined to assess the economic feasibility of the proposed e-HPS for the truck lifespan, using numerical simulations. The aim was twofold: To provide an implementation method to retrofit the e-HPS to a conventional waste compactor truck and to assess its economic feasibility, investigating fuel savings during the use phase and the consequent reduction of CO2 emissions. Results show that the total lifespan cost saving achieved a value of 65,000. Furthermore, total CO2 emissions for the e-HPS were about 80% lower than those of the ICE-HPS, highlighting that the e-HPS can provide significant environmental benefits in an urban context

Interacting Discovery Processes on Complex Networks

Innovation is the driving force of human progress. Recent urn models reproduce well the dynamics through which the discovery of a novelty may trigger further ones, in an expanding space of opportunities, but neglect the effects of social interactions. Here we focus on the mechanisms of collective exploration and we propose a model in which many urns, representing different explorers, are coupled through the links of a social network and exploit opportunities coming from their contacts. We study different network structures showing, both analytically and numerically, that the pace of discovery of an explorer depends on its centrality in the social network. Our model sheds light on the role that social structures play in discovery processes

Global performance index for integrated management system: GPI-IMS

Background: The present work starts from a literature review of the evolution of Integrated Management Systems (IMSs), considering different points of view and standards: quality, environmental, occupational health and safety, sustainability and social issues. Even if the benefits are possible, there is not a common approach and a clear link between the integration of management systems and business performance, in particular considering safety performance. Methods: The present study analyzes the application of Risk Assessment in order to realize the integration of management systems. The main objective is to provide a tool for an integrated evaluation of all company performances, starting from the definition of some Key Performance Indicators—KPIs— proposed for a particular case study, even if their choice is not the core of the paper. The assessment team members on the basis of their knowledge, experience and useful literature, could choose the right KPIs for the specific application, able to take a picture of the current state and to suggest a possible recommended action of improving. The proposed Risk Assessment approach is an integration of modern management techniques: Integrated Management System and Improving Cycle DMAIC. Results: The new method, called the Global Performance Index for Integrated Management System—GPI-IMS, has been applied to a real case study in the logistic field in order to evaluate its goodness and possible generalization. Conclusions: The proposed method allows to define the requirements that any company must have to perform the best. The role of the assessment team is very important to evaluate the global performance of the company and to suggest the corrective actions to be adopted

Quality Checks Logit Human Reliability (LHR): A New Model to Evaluate Human Error Probability (HEP)

In the years, several approaches for human reliability analysis (HRA) have been developed. The aim of the present research is to propose a hybrid model to evaluate Human Error Probability (HEP). The new approach is based on logit-normal distribution, Nuclear Action Reliability Assessment (NARA), and Performance Shaping Factors (PSFs) relationship. In the research, shortcomings related to literature approaches are analyzed, especially the limitations of the working time. For this reason, PSFs after 8 hours (work standard) during emergency conditions were estimated. Therefore, the correlation between the advantages of these three methodologies allows proposing a HEP analysis during accident scenarios and emergencies; a fundamental issue to ensure the safety and reliability in industrial plants is emergency Mmnagement (EM). Applying EM methodology, two main aspects are analyzed: system reliability and human reliability. System reliability is strongly related to the reliability of its weakest component. During incidental situations, the weakest parts of the whole system are workers (human reliability) and accidental scenarios influence the operator's ability to make decisions. This article proposes a new approach called Logit Human Reliability (LHR) that considers internal and external factors to estimate human reliability during emergencies. LHR has been applied in a pharmaceutical accident scenario, considering 24 hours of working time (more than 8 working hours). The results highlighted that the LHR method gives output data more in conformity with data banks than the conventional methods during the stress phase in an accident scenario

A hybrid model to evaluate human error probability (HEP) in a pharmaceutical plant

The aim of the present research is to propose a hybrid model to evaluate Human Error Probability (HEP) called Logit Human Reliability (LHR). The new approach is based on logit normal distribution, Nuclear Action Reliability Assessment (NARA), and Performance Shaping Factors (PSFs) relationship. The present paper analyzed some shortcomings related to literature approaches, especially the limitations of the working time. We estimated PSFs after 8 hours (work standard) during emergency conditions. Therefore, the correlation between the advantages of these three methodologies allows proposing a HEP analysis during accident scenario and emergencies. The proposed approach considers internal and external factors that affect the operator's ability. LHR has been applied in a pharmaceutical accident scenario, considering 24 hours of working time (more than 8 working hours)

Reliability estimation of reinforced slopes to prioritize maintenance actions

Geosynthetics are extensively utilized to improve the stability of geotechnical structures and slopes in urban areas. Among all existing geosynthetics, geotextiles are widely used to reinforce unstable slopes due to their capabilities in facilitating reinforcement and drainage. To reduce settlement and increase the bearing capacity and slope stability, the classical use of geotextiles in embankments has been suggested. However, several catastrophic events have been reported, including failures in slopes in the absence of geotextiles. Many researchers have studied the stability of geotextile-reinforced slopes (GRSs) by employing different methods (analytical models, numerical simulation, etc.). The presence of source-to-source uncertainty in the gathered data increases the complexity of evaluating the failure risk in GRSs since the uncertainty varies among them. Consequently, developing a sound methodology is necessary to alleviate the risk complexity. Our study sought to develop an advanced risk-based maintenance (RBM) methodology for prioritizing maintenance operations by addressing fluctuations that accompany event data. For this purpose, a hierarchical Bayesian approach (HBA) was applied to estimate the failure probabilities of GRSs. Using Markov chain Monte Carlo simulations of likelihood function and prior distribution, the HBA can incorporate the aforementioned uncertainties. The proposed method can be exploited by urban designers, asset managers, and policymakers to predict the mean time to failures, thus directly avoiding unnecessary maintenance and safety consequences. To demonstrate the application of the proposed methodology, the performance of nine reinforced slopes was considered. The results indicate that the average failure probability of the system in an hour is 2.8 ≥ 105 during its lifespan, which shows that the proposed evaluation method is more realistic than the traditional methods