Charge accumulation and transport in degenerately doped semiconducting polymers with mixed ionic and electronic conductivity

This thesis is part of the fields of Material Physics and Organic Electronics and aims to determine the charge carrier density and mobility in the hydrated conducting polymer–polyelectrolyte blend PEDOT:PSS. This kind of material combines electronic semiconductor functionality with selective ionic transport, biocompatibility and electrochemical stability in water. This advantageous material properties combination makes PEDOT:PSS a unique material to build organic electrochemical transistors (OECTs), which have relevant application as amplifying transducers for bioelectronic signals. In order to measure charge carrier density and mobility, an innovative 4-wire, contact independent characterization technique was introduced, the electrolyte-gated van der Pauw (EgVDP) method, which was combined with electrochemical impedance spectroscopy. The technique was applied to macroscopic thin film samples and micro-structured PEDOT:PSS thin film devices fabricated using photolithography. The EgVDP method revealed to be effective for the measurements of holes’ mobility in hydrated PEDOT:PSS thin films, which resulted to be <μ>=(0.67±0.02) cm^2/(V*s). By comparing this result with 2-point-probe measurements, we found that contact resistance effects led to a mobility overestimation in the latter. Ion accumulation at the drain contact creates a gate-dependent potential barrier and is discussed as a probable reason for the overestimation in 2-point-probe measurements. The measured charge transport properties of PEDOT:PSS were analyzed in the framework of an extended drift-diffusion model. The extended model fits well also to the non-linear response in the transport characterization and results suggest a Gaussian DOS for PEDOT:PSS. The PEDOT:PSS-electrolyte interface capacitance resulted to be voltage-independent, confirming the hypothesis of its morphological origin, related to the separation between the electronic (PEDOT) and ionic (PSS) phases in the blend.

From point cloud to HBIM: an integrated workflow for 3D reconstruction of heritage building with model accuracy evaluation

Modern society is now facing significant difficulties in attempting to preserve its architectural heritage. Numerous challenges arise consequently when it comes to documentation, preservation and restoration. Fortunately, new perspectives on architectural heritage are emerging owing to the rapid development of digitalization. Therefore, this presents new challenges for architects, restorers and specialists. Additionally, this has changed the way they approach the study of existing heritage, changing from conventional 2D drawings in response to the increasing requirement for 3D representations. Recently, Building Information Modelling for historic buildings (HBIM) has escalated as an emerging trend to interconnect geometrical and informational data. Currently, the latest 3D geomatics techniques based on 3D laser scanners with enhanced photogrammetry along with the continuous improvement in the BIM industry allow for an enhanced 3D digital reconstruction of historical and existing buildings. This research study aimed to develop an integrated workflow for the 3D digital reconstruction of heritage buildings starting from a point cloud. The Pieve of San Michele in Acerboli’s Church in Santarcangelo Di Romagna (6th century) served as the test bed. The point cloud was utilized as an essential referential to model the BIM geometry using Autodesk Revit® 2022. To validate the accuracy of the model, Deviation Analysis Method was employed using CloudCompare software to determine the degree of deviation between the HBIM model and the point cloud. The acquired findings showed a very promising outcome in the average distance between the HBIM model and the point cloud. The conducted approach in this study demonstrated the viability of producing a precise BIM geometry from point clouds.

The Libor Market Model: from theory to calibration

This thesis is focused on the financial model for interest rates called the LIBOR Market Model. In the appendixes, we provide the necessary mathematical theory. In the inner chapters, firstly, we define the main interest rates and financial instruments concerning with the interest rate models, then, we set the LIBOR market model, demonstrate its existence, derive the dynamics of forward LIBOR rates and justify the pricing of caps according to the Black’s formula. Then, we also present the Swap Market Model, which models the forward swap rates instead of the LIBOR ones. Even this model is justified by a theoretical demonstration and the resulting formula to price the swaptions coincides with the Black’s one. However, the two models are not compatible from a theoretical point. Therefore, we derive various analytical approximating formulae to price the swaptions in the LIBOR market model and we explain how to perform a Monte Carlo simulation. Finally, we present the calibration of the LIBOR market model to the markets of both caps and swaptions, together with various examples of application to the historical correlation matrix and the cascade calibration of the forward volatilities to the matrix of implied swaption volatilities provided by the market.

Circumplex Model to analyze the impact of emotions on decision-making processes in innovation management

Group work allows participants to pool their thoughts and examine difficulties from several angles. In these settings, it is possible to attempt things that an individual could not achieve, combining a variety of abilities and knowledge to tackle more complicated and large-scale challenges. That’s why nowadays collaborative work is becoming more and more widespread to solve complex innovation dilemmas. Since innovation isn’t a tangible thing, most innovation teams used to take decisions based on performance KPIs such as forecasted engagement, projected profitability, investments required, cultural impacts etc. Have you ever wondered the reason why sometimes innovation group processes come out with decisions which are not the optimal meeting point of all the KPIs? Has this decision been influenced by other factors? Some researchers account part of this phenomenon to the emotions in group-based interaction between participants. I will develop a literature review that is split into three parts: first, I will consider some emotions theories from an individual perspective; secondly, a wider view of collective interactions theories will be provided; lastly, I will supply some recent collective interaction empirical studies. After the theoretical and empirical gaps have been tackled, the study will additionally move forward with a methodological point of view, about the Circumplex Model, which is the model I used to evaluate emotions in my research. This model has been applied to SUGAR project, which is the biggest design thinking academy worldwide.

Root cause analysis applied to a finite element model's refinement of a negative stiffness structure

Negative Stiffness Structures are mechanical systems that require a decrease in the applied force to generate an increase in displacement. They are structures that possess special characteristics such as snap-through and bi-stability. All of these features make them particularly suitable for different applications, such as shock-absorption, vibration isolation and damping. From this point of view, they have risen awareness of their characteristics and, in order to match them to the application needed, a numerical simulation is of great interest. In this regard, this thesis is a continuation of previous studies in a circular negative stiffness structure and aims at refine the numerical model by presenting a new solution. To that end, an investigation procedure is needed. Amongst all of the methods available, root cause analysis was the chosen one to perform the investigation since it provides a clear view of the problem under analysis and a categorization of all the causes behind it. As a result of the cause-effect analysis, the main causes that have influence on the numerical results were obtained. Once all of the causes were listed, solutions to them were proposed and it led to a new numerical model. The numerical model proposed was of nonlinear type of analysis with hexagonal elements and a hyperelastic material model. The results were analyzed through force-displacement curves, allowing for the visualization of the structure’s energy recovery. When compared to the results obtained from the experimental part, it is evident that the trend is similar and the negative stiffness behaviour is present.

Tree regularized neural networks for key point analysis

This thesis contributes to the ArgMining 2021 shared task on Key Point Analysis. Key Point Analysis entails extracting and calculating the prevalence of a concise list of the most prominent talking points, from an input corpus. These talking points are usually referred to as key points. Key point analysis is divided into two subtasks: Key Point Matching, which involves assigning a matching score to each key point/argument pair, and Key Point Generation, which consists of the generation of key points. The task of Key Point Matching was approached using different models: a pretrained Sentence Transformers model and a tree-constrained Graph Neural Network were tested. The best model was the fine-tuned Sentence Transformers, which achieved a mean Average Precision score of 0.75, ranking 12 compared to other participating teams. The model was then used for the subtask of Key Point Generation using the extractive method in the selection of key point candidates and the model developed for the previous subtask to evaluate them.