Inverse Static Analysis of Massive Parallel Arrays of Three-State Actuators via Artificial Intelligence

Massive parallel robots (MPRs) driven by discrete actuators are force regulated robots that undergo continuous motions despite being commanded through a finite number of states only. Designing a real-time control of such systems requires fast and efficient methods for solving their inverse static analysis (ISA), which is a challenging problem and the subject of this thesis. In particular, five Artificial intelligence methods are proposed to investigate the on-line computation and the generalization error of ISA problem of a class of MPRs featuring three-state force actuators and one degree of revolute motion.

Hybrid Artificial Intelligence to Extract Patterns and Rules from Argumentative and Legal Texts

This Thesis is composed of a collection of works written in the period 2019-2022, whose aim is to find methodologies of Artificial Intelligence (AI) and Machine Learning to detect and classify patterns and rules in argumentative and legal texts. We define our approach “hybrid”, since we aimed at designing hybrid combinations of symbolic and sub-symbolic AI, involving both “top-down” structured knowledge and “bottom-up” data-driven knowledge. A first group of works is dedicated to the classification of argumentative patterns. Following the Waltonian model of argument and the related theory of Argumentation Schemes, these works focused on the detection of argumentative support and opposition, showing that argumentative evidences can be classified at fine-grained levels without resorting to highly engineered features. To show this, our methods involved not only traditional approaches such as TFIDF, but also some novel methods based on Tree Kernel algorithms. After the encouraging results of this first phase, we explored the use of a some emerging methodologies promoted by actors like Google, which have deeply changed NLP since 2018-19 — i.e., Transfer Learning and language models. These new methodologies markedly improved our previous results, providing us with best-performing NLP tools. Using Transfer Learning, we also performed a Sequence Labelling task to recognize the exact span of argumentative components (i.e., claims and premises), thus connecting portions of natural language to portions of arguments (i.e., to the logical-inferential dimension). The last part of our work was finally dedicated to the employment of Transfer Learning methods for the detection of rules and deontic modalities. In this case, we explored a hybrid approach which combines structured knowledge coming from two LegalXML formats (i.e., Akoma Ntoso and LegalRuleML) with sub-symbolic knowledge coming from pre-trained (and then fine-tuned) neural architectures.

Design and testing of innovative, artificial intelligence-based techniques to model and control the combustion process and to reduce the Co2 emissions in modern, high-performance, gasoline direct injection engines

This work deals with the development of calibration procedures and control systems to improve the performance and efficiency of modern spark ignition turbocharged engines. The algorithms developed are used to optimize and manage the spark advance and the air-to-fuel ratio to control the knock and the exhaust gas temperature at the turbine inlet. The described work falls within the activity that the research group started in the previous years with the industrial partner Ferrari S.p.a. . The first chapter deals with the development of a control-oriented engine simulator based on a neural network approach, with which the main combustion indexes can be simulated. The second chapter deals with the development of a procedure to calibrate offline the spark advance and the air-to-fuel ratio to run the engine under knock-limited conditions and with the maximum admissible exhaust gas temperature at the turbine inlet. This procedure is then converted into a model-based control system and validated with a Software in the Loop approach using the engine simulator developed in the first chapter. Finally, it is implemented in a rapid control prototyping hardware to manage the combustion in steady-state and transient operating conditions at the test bench. The third chapter deals with the study of an innovative and cheap sensor for the in-cylinder pressure measurement, which is a piezoelectric washer that can be installed between the spark plug and the engine head. The signal generated by this kind of sensor is studied, developing a specific algorithm to adjust the value of the knock index in real-time. Finally, with the engine simulator developed in the first chapter, it is demonstrated that the innovative sensor can be coupled with the control system described in the second chapter and that the performance obtained could be the same reachable with the standard in-cylinder pressure sensors.

Anticancer drug discovery using artificial intelligence: an application in pharmacological activity prediction

Hematological cancers are a heterogeneous family of diseases that can be divided into leukemias, lymphomas, and myelomas, often called “liquid tumors”. Since they cannot be surgically removable, chemotherapy represents the mainstay of their treatment. However, it still faces several challenges like drug resistance and low response rate, and the need for new anticancer agents is compelling. The drug discovery process is long-term, costly, and prone to high failure rates. With the rapid expansion of biological and chemical "big data", some computational techniques such as machine learning tools have been increasingly employed to speed up and economize the whole process. Machine learning algorithms can create complex models with the aim to determine the biological activity of compounds against several targets, based on their chemical properties. These models are defined as multi-target Quantitative Structure-Activity Relationship (mt-QSAR) and can be used to virtually screen small and large chemical libraries for the identification of new molecules with anticancer activity. The aim of my Ph.D. project was to employ machine learning techniques to build an mt-QSAR classification model for the prediction of cytotoxic drugs simultaneously active against 43 hematological cancer cell lines. For this purpose, first, I constructed a large and diversified dataset of molecules extracted from the ChEMBL database. Then, I compared the performance of different ML classification algorithms, until Random Forest was identified as the one returning the best predictions. Finally, I used different approaches to maximize the performance of the model, which achieved an accuracy of 88% by correctly classifying 93% of inactive molecules and 72% of active molecules in a validation set. This model was further applied to the virtual screening of a small dataset of molecules tested in our laboratory, where it showed 100% accuracy in correctly classifying all molecules. This result is confirmed by our previous in vitro experiments.

Anatomical biomarkers of body composition in oncological population: application and development of artificial intelligences

In the Era of precision medicine and big medical data sharing, it is necessary to solve the work-flow of digital radiological big data in a productive and effective way. In particular, nowadays, it is possible to extract information “hidden” in digital images, in order to create diagnostic algorithms helping clinicians to set up more personalized therapies, which are in particular targets of modern oncological medicine. Digital images generated by the patient have a “texture” structure that is not visible but encrypted; it is “hidden” because it cannot be recognized by sight alone. Thanks to artificial intelligence, pre- and post-processing software and generation of mathematical calculation algorithms, we could perform a classification based on non-visible data contained in radiological images. Being able to calculate the volume of tissue body composition could lead to creating clasterized classes of patients inserted in standard morphological reference tables, based on human anatomy distinguished by gender and age, and maybe in future also by race. Furthermore, the branch of “morpho-radiology" is a useful modality to solve problems regarding personalized therapies, which is particularly needed in the oncological field. Actually oncological therapies are no longer based on generic drugs but on target personalized therapy. The lack of gender and age therapies table could be filled thanks to morpho-radiology data analysis application.

The photosynthetic bacterial reaction center in native and artificial envirnoments: effects on light-induced electron transfer

In this thesis we focussed on the characterization of the reaction center (RC) protein purified from the photosynthetic bacterium Rhodobacter sphaeroides. In particular, we discussed the effects of native and artificial environment on the light-induced electron transfer processes. The native environment consist of the inner antenna LH1 complex that copurifies with the RC forming the so called core complex, and the lipid phase tightly associated with it. In parallel, we analyzed the role of saccharidic glassy matrices on the interplay between electron transfer processes and internal protein dynamics. As a different artificial matrix, we incorporated the RC protein in a layer-by-layer structure with a twofold aim: to check the behaviour of the protein in such an unusual environment and to test the response of the system to herbicides. By examining the RC in its native environment, we found that the light-induced charge separated state P+QB - is markedly stabilized (by about 40 meV) in the core complex as compared to the RC-only system over a physiological pH range. We also verified that, as compared to the average composition of the membrane, the core complex copurifies with a tightly bound lipid complement of about 90 phospholipid molecules per RC, which is strongly enriched in cardiolipin. In parallel, a large ubiquinone pool was found in association with the core complex, giving rise to a quinone concentration about ten times larger than the average one in the membrane. Moreover, this quinone pool is fully functional, i.e. it is promptly available at the QB site during multiple turnover excitation of the RC. The latter two observations suggest important heterogeneities and anisotropies in the native membranes which can in principle account for the stabilization of the charge separated state in the core complex. The thermodynamic and kinetic parameters obtained in the RC-LH1 complex are very close to those measured in intact membranes, indicating that the electron transfer properties of the RC in vivo are essentially determined by its local environment. The studies performed by incorporating the RC into saccharidic matrices evidenced the relevance of solvent-protein interactions and dynamical coupling in determining the kinetics of electron transfer processes. The usual approach when studying the interplay between internal motions and protein function consists in freezing the degrees of freedom of the protein at cryogenic temperature. We proved that the “trehalose approach” offers distinct advantages with respect to this traditional methodology. We showed, in fact, that the RC conformational dynamics, coupled to specific electron transfer processes, can be modulated by varying the hydration level of the trehalose matrix at room temperature, thus allowing to disentangle solvent from temperature effects. The comparison between different saccharidic matrices has revealed that the structural and dynamical protein-matrix coupling depends strongly upon the sugar. The analyses performed in RCs embedded in polyelectrolyte multilayers (PEM) structures have shown that the electron transfer from QA - to QB, a conformationally gated process extremely sensitive to the RC environment, can be strongly modulated by the hydration level of the matrix, confirming analogous results obtained for this electron transfer reaction in sugar matrices. We found that PEM-RCs are a very stable system, particularly suitable to study the thermodynamics and kinetics of herbicide binding to the QB site. These features make PEM-RC structures quite promising in the development of herbicide biosensors. The studies discussed in the present thesis have shown that, although the effects on electron transfer induced by the native and artificial environments tested are markedly different, they can be described on the basis of a common kinetic model which takes into account the static conformational heterogeneity of the RC and the interconversion between conformational substates. Interestingly, the same distribution of rate constants (i.e. a Gamma distribution function) can describe charge recombination processes in solutions of purified RC, in RC-LH1 complexes, in wet and dry RC-PEM structures and in glassy saccharidic matrices over a wide range of hydration levels. In conclusion, the results obtained for RCs in different physico-chemical environments emphasize the relevance of the structure/dynamics solvent/protein coupling in determining the energetics and the kinetics of electron transfer processes in a membrane protein complex.