Valutazione cinematica complessiva, in vivo e sotto carico, di un modello protesico di caviglia a 3 componenti.

Nonostante le importanti ricadute che gli impianti protesici di caviglia hanno nella qualità della vita dei pazienti che si sottopongono ad intervento di sostituzione articolare, le reali proprietà biomeccaniche e cinematiche in-vivo e sotto carico degli impianti protesici sono state scarsamente studiate e descritte in letteratura. Lo scopo di questa trattazione è quella di valutare la cinematica protesica complessiva, in vivo, attraverso l’utilizzo dell’Analisi Radiostereometrica model-based (MB-RSA) e di ulteriori metodiche clinico-strumentali. La valutazione cinematica è stata permessa dall’analisi della posizione degli impianti attraverso la MB-RSA. Tra gli obiettivi secondari, i pazienti sono stati valutati clinicamente mediante AOFAS Ankle-Hindfoot score e SF-36, mediante full-body gait analysis con sensori inerziali e valutazione posturale-stabilometrica mediante Y Balance Test e workstation dedicata Delos DPPS. I pazienti sottoposti ad iter completo con valutazione clinica e strumentale a fine follow-up sono risultati 18 (2 drop-out). Il ROM complessivo a catena cinetica chiusa ha evidenziato una dorsi-plantarflessione complessiva media di 19.84°. Gli score clinici hanno mostrato tutti un netto miglioramento nel post-operatorio. La gait analysis ha evidenziato uno schema del passo composto dai tre principali spike e compatibile con schemi fisiologici. Dal punto di vista cinematico, i risultati angolari MB-RSA ricavati durante questo lavoro di tesi evidenziano tutti e 6 i gradi di libertà, dato coerente con la mobilità di una caviglia nativa. Valori di articolarità differenti sono stati registrati mediante sensori inerziali. Infine, in una valutazione cinematica complessiva, le possibili implicazioni sul bilanciamento posturale e propriocettivo presente nelle caviglie artrosiche e successivamente sottoposte a sostituzione protesica totale sono ampiamente descritte e discusse. I dati raccolti in questo lavoro di tesi rappresentano il risultato di una valutazione cinematica complessiva, e potranno aiutare a definire una tipologia di soggetto artrosico in cui i risultati siano verosimilmente migliori ed eventualmente a migliorare design e strumentari futuri.

Governing the water conservation in the Urmia Lake Basin: addressing macro systems’ fit and micro users’ behavior

To change unadapted water governing systems, and water users’ traditional conducts in line with climate change, understanding of systems’ structures and users’ behaviors is necessary. To this aim, comprehensive and pragmatic research was designed and implemented in the Urmia Lake Basin where due to the severe droughts, and human-made influences, especially through the agricultural development, the lake has been shrunken drastically. To analyze the water governance and conservation issues in the basin, an innovative framework was developed based on mathematical physics concepts and pro-environmental behavior theories. Accordingly, in system level (macro/meso), the problem of fit of the early-shaped water governing system associating with the function of “political-security” and “political-economic” factors in the basin was identified through mean-field models. Furthermore, the effect of a “political-environmental” factor, the Urmia Lake Restoration Program (ULRP), on reforming the system structure and hence its fit was assessed. The analysis results revealed that by revising the provincial boundaries (horizontal alternation) for the entity of Kurdistan province to permit that interact with the headquarter of West Azerbaijan province for its water demand-supply initiatives, the system fit can increase. Also, the constitution of the ULRP (vertical arrangement) not only could increase the structural fit of the water governing system to the basin, but also significantly could enhance the system fit through its water-saving policy. Besides, in individual level (micro), the governing factors of water conservation behavior of the major users/farmers were identified through rational and moral socio-psychological models. In rational approach, incorporating PMT and TPB, the SEM results demonstrated that “Perceived Vulnerability”, “Self-Efficacy”, “Response Efficacy”, “Response Cost”, “Subjective Norms” and “Institutional Trust” significantly affect the water-saving intention/behavior. Likewise, NAM based analysis as a moral approach, uncovered the significant effects of “Awareness of Consequences”, “Appraisal of Responsibility”, “Personal Norms” as well as “Place Attachment” and “Emotions” on water-saving intention.

Novel methods of state estimation and parameter identification for high-performance motorcycles

This research activity aims at providing a reliable estimation of particular state variables or parameters concerning the dynamics and performance optimization of a MotoGP-class motorcycle, integrating the classical model-based approach with new methodologies involving artificial intelligence. The first topic of the research focuses on the estimation of the thermal behavior of the MotoGP carbon braking system. Numerical tools are developed to assess the instantaneous surface temperature distribution in the motorcycle's front brake discs. Within this application other important brake parameters are identified using Kalman filters, such as the disc convection coefficient and the power distribution in the disc-pads contact region. Subsequently, a physical model of the brake is built to estimate the instantaneous braking torque. However, the results obtained with this approach are highly limited by the knowledge of the friction coefficient (μ) between the disc rotor and the pads. Since the value of μ is a highly nonlinear function of many variables (namely temperature, pressure and angular velocity of the disc), an analytical model for the friction coefficient estimation appears impractical to establish. To overcome this challenge, an innovative hybrid solution is implemented, combining the benefit of artificial intelligence (AI) with classical model-based approach. Indeed, the disc temperature estimated through the thermal model previously implemented is processed by a machine learning algorithm that outputs the actual value of the friction coefficient thus improving the braking torque computation performed by the physical model of the brake. Finally, the last topic of this research activity regards the development of an AI algorithm to estimate the current sideslip angle of the motorcycle's front tire. While a single-track motorcycle kinematic model and IMU accelerometer signals theoretically enable sideslip calculation, the presence of accelerometer noise leads to a significant drift over time. To address this issue, a long short-term memory (LSTM) network is implemented.