An Innovation System Perspective on Adaptation Strategies to Climate Variability and Water Management in India

Government policies play a critical role in influencing market conditions, institutions and overall agricultural productivity. The thesis therefore looks into the history of agriculture development in India. Taking a political economy perspective, the historical account looks at significant institutional and technological innovations carried out in pre- independent and post independent India. It further focuses on the Green Revolution in Asia, as forty years after; the agricultural community still faces the task of addressing recurrent issue of food security amidst emerging challenges, such as climate change. It examines the Green Revolution that took place in India during the late 1960s and 70s in a historical perspective, identifying two factors of institutional change and political leadership. Climate change in agriculture development has become a major concern to farmers, researchers and policy makers alike. However, there is little knowledge on the farmers’ perception to climate change and to the extent they coincide with actual climatic data. Using a qualitative approach,it looks into the perceptions of the farmers in four villages in the states of Maharashtra and Andhra Pradesh. While exploring the adaptation strategies, the chapter looks into the dynamics of who can afford a particular technology and who cannot and what leads to a particular adaptation decision thus determining the adaptive capacity in water management. The final section looks into the devolution of authority for natural resource management to local user groups through the Water Users’ Associations as an important approach to overcome the long-standing challenges of centralized state bureaucracies in India. It addresses the knowledge gap of why some local user groups are able to overcome governance challenges such as elite capture, while others-that work under the design principles developed by Elinor Ostrom. It draws conclusions on how local leadership, can be promoted to facilitate participatory irrigation management.

Advanced decision-support methods for the design, control, and optimization of perishable products life cycle

In the last decades, global food supply chains had to deal with the increasing awareness of the stakeholders and consumers about safety, quality, and sustainability. In order to address these new challenges for food supply chain systems, an integrated approach to design, control, and optimize product life cycle is required. Therefore, it is essential to introduce new models, methods, and decision-support platforms tailored to perishable products. This thesis aims to provide novel practice-ready decision-support models and methods to optimize the logistics of food items with an integrated and interdisciplinary approach. It proposes a comprehensive review of the main peculiarities of perishable products and the environmental stresses accelerating their quality decay. Then, it focuses on top-down strategies to optimize the supply chain system from the strategical to the operational decision level. Based on the criticality of the environmental conditions, the dissertation evaluates the main long-term logistics investment strategies to preserve products quality. Several models and methods are proposed to optimize the logistics decisions to enhance the sustainability of the supply chain system while guaranteeing adequate food preservation. The models and methods proposed in this dissertation promote a climate-driven approach integrating climate conditions and their consequences on the quality decay of products in innovative models supporting the logistics decisions. Given the uncertain nature of the environmental stresses affecting the product life cycle, an original stochastic model and solving method are proposed to support practitioners in controlling and optimizing the supply chain systems when facing uncertain scenarios. The application of the proposed decision-support methods to real case studies proved their effectiveness in increasing the sustainability of the perishable product life cycle. The dissertation also presents an industry application of a global food supply chain system, further demonstrating how the proposed models and tools can be integrated to provide significant savings and sustainability improvements.

Exploiting the environment for structural health monitoring: Strategies, algorithms, and technologies

A densely built environment is a complex system of infrastructure, nature, and people closely interconnected and interacting. Vehicles, public transport, weather action, and sports activities constitute a manifold set of excitation and degradation sources for civil structures. In this context, operators should consider different factors in a holistic approach for assessing the structural health state. Vibration-based structural health monitoring (SHM) has demonstrated great potential as a decision-supporting tool to schedule maintenance interventions. However, most excitation sources are considered an issue for practical SHM applications since traditional methods are typically based on strict assumptions on input stationarity. Last-generation low-cost sensors present limitations related to a modest sensitivity and high noise floor compared to traditional instrumentation. If these devices are used for SHM in urban scenarios, short vibration recordings collected during high-intensity events and vehicle passage may be the only available datasets with a sufficient signal-to-noise ratio. While researchers have spent efforts to mitigate the effects of short-term phenomena in vibration-based SHM, the ultimate goal of this thesis is to exploit them and obtain valuable information on the structural health state. First, this thesis proposes strategies and algorithms for smart sensors operating individually or in a distributed computing framework to identify damage-sensitive features based on instantaneous modal parameters and influence lines. Ordinary traffic and people activities become essential sources of excitation, while human-powered vehicles, instrumented with smartphones, take the role of roving sensors in crowdsourced monitoring strategies. The technical and computational apparatus is optimized using in-memory computing technologies. Moreover, identifying additional local features can be particularly useful to support the damage assessment of complex structures. Thereby, smart coatings are studied to enable the self-sensing properties of ordinary structural elements. In this context, a machine-learning-aided tomography method is proposed to interpret the data provided by a nanocomposite paint interrogated electrically.