Optimal Computational Design Framework for Modular Manipulators

Modular manipulators, composed of multiple independent modules, offer a versatile solution to meet the growing need for mass-customized manipulation tasks. These robots can be swiftly reconfigured into various morphologies by altering the interconnections between the modules. This flexibility allows them to adapt seamlessly to a wide array of tasks and environments. The capability to rearrange these modules into diverse configurations enhances the robot’s adaptability, making it highly effect...

From Earth to Sky and Beyond: Sampling for Scientific and Industrial Innovation

Accurate modeling and forecasting are essential for decision making in agriculture and atmospheric sciences. First, a stochastic weather generator trained on more than 60 years of hourly data produces synthetic climate scenarios that drive a deterministic wheat growth model to produce probabilistic distributions of average and extreme crop outcomes. A web application allows users to upload regional weather data and obtain customized forecasts for risk management and optimization. Second, size...

Valorization of side-streams from the food industry through the use of green technologies

This paper summarizes my three-year doctoral thesis project at the Food and Dietary Products Laboratory of the Department of Pharmacy (DIFAR) of the University of Genoa (Italy) with the collaboration of the Sustainability & Circular Economy Team of ANFACO-CECOPESCA (Vigo Spain), the Synthesis Team of AIMPLAS (Valencia, Spain), the Department of Marine Biotechnology of NOFIMA (Tromsø, Norway), Environmental and Molecular Microbiology Team of MICAMO Lab (Genoa, Italy), the Food Chemistry Gr...

Neural radiance fields as a regularization approach to inverse volume rendering

Inverse volume rendering poses a significant challenge in reconstructing density and color functions from real-world observations, particularly due to the non injectivity of the problem. This study addresses this issue by introducing a unique criterion to ensure a well-defined solution. We explore the connection between classical regularization, as well as the selection method, and modern neural network approaches. Our methodology integrates minimum support regularization with a novel techni...

Modelling the Dynamic Impacts of Consecutive Natural Hazards: A Framework for Time-Evolving Vulnerability and Recovery Analysis

In an increasingly complex hazard landscape, understanding how physical damage evolves under multiple, interacting natural hazards has become a critical challenge for disaster risk assessment. When hazards occur simultaneously or in close succession, their combined impacts can severely compromise the physical integrity of exposed assets, often amplifying damage beyond what would be expected if each event were considered independently. Although the importance of these interactions is now ...

Graph-Based Approximation and Analysis of Traffic Data on Mobility Networks

Urban mobility is a pillar in geographical systems and smart cities. The understanding of the parts and their interrelated movement within urban regions or city networks is essential for comprehending complex spatial dynamics and making informed decisions in urban planning and traffic control. The limitations of various mobility and traffic data collection methods make it challenging to explain movement across an entire area of interest, underscoring the need for methods to approximate values...

Shaping the Developing Brain: The Impact of Environmental Factors on At-risk Paediatric Population

This doctoral thesis investigates how modifiable environmental factors influence neurodevelopment in paediatric populations with heightened neurological risk. Adopting a multidisciplinary approach, the study examines three interconnected domains: the planetary environment, the family environment, and the neonatal intensive care unit (NICU) setting. The first section evaluates the impact of climate change, specifically, temperature fluctuations, air pollution (notably PM2.5), and extreme weat...

Development of porous materials with tailored structural and functional properties

Le nanoparticelle (NPs) hanno un grande potenziale per diverse applicazioni, tra cui la catalisi, l'adsorbimento di gas e la filtrazione di aria e liquidi. Comunque, per sfruttare appieno le loro capacità è spesso necessario assemblarle in architetture funzionali di maggiori dimensioni. Tra queste strutture, le schiume solide porose offrono vantaggi unici grazie alla loro elevata area superficiale, alla bassa densità e alle strutture interconnesse. Questa ricerca di dottorato affronta lo svi...

Multifunctional Nanomaterials for Tumor Diagnosis and Treatment