Morphological and physiological responses of apple trees under photoselective colored nets

In recent years and thanks to innovative technological advances in supplemental lighting sources and photo-selective filters, light quality manipulation (i.e. spectral composition of sunlight) have demonstrated positive effects on plant performance in ornamentals and vegetable crops. However, this aspect has been much less studied in fruit trees due to the difficulty of conditioning the light environment of orchards. The aim of the present PhD research was to study the use of different colored nets with selective light transmission in the blue (400 – 500 nm), red (600 – 700 nm) and near infrared (700 – 1100 nm) wavelengths as a tool to the light quality management and its morphological and physiological effects in field-grown apple trees. Chapter I provides a review the current status on physiological and technological advances on light quality management in fruit trees. Chapter II shows the main effect of colored nets on morpho-anatomical (stomata density, mesophyll structure and leaf mass area index) characteristics in apple leaves. Chapter III provides an analysis about the effect of micro-environmental conditions under colored nets on leaf stomatal conductance and leaf photosynthetic capacity. Chapter IV describes a study approach to evaluate the impact of colored nets on fruit growth potential in apples. Summing up results obtained in the present PhD dissertation clearly demonstrate that light quality management through photo-selective colored nets presents an interesting potential for the manipulation of plant morphological and physiological traits in apple trees. Cover orchards with colored nets might be and alternative technology to address many of the most important challenges of modern fruit growing, such as: the need for the efficient use of natural resources (water, soil and nutrients) the reduction of environmental impacts and the mitigation of possible negative effects of global climate change.

International Agricultural Trade under Regulatory Asymmetry: An Economic Analysis of SME Export Behavior

Over the last three decades, international agricultural trade has grown significantly. Technological advances in transportation logistics and storage have created opportunities to ship anything almost anywhere. Bilateral and multilateral trade agreements have also opened new pathways to an increasingly global market place. Yet, international agricultural trade is often constrained by differences in regulatory regimes. The impact of “regulatory asymmetry” is particularly acute for small and medium sized enterprises (SMEs) that lack resources and expertise to successfully operate in markets that have substantially different regulatory structures. As governments seek to encourage the development of SMEs, policy makers often confront the critical question of what ultimately motivates SME export behavior. Specifically, there is considerable interest in understanding how SMEs confront the challenges of regulatory asymmetry. Neoclassical models of the firm generally emphasize expected profit maximization under uncertainty, however these approaches do not adequately explain the entrepreneurial decision under regulatory asymmetry. Behavioral theories of the firm offer a far richer understanding of decision making by taking into account aspirations and adaptive performance in risky environments. This paper develops an analytical framework for decision making of a single agent. Considering risk, uncertainty and opportunity cost, the analysis focuses on the export behavior response of an SME in a situation of regulatory asymmetry. Drawing on the experience of fruit processor in Muzaffarpur, India, who must consider different regulatory environments when shipping fruit treated with sulfur dioxide, the study dissects the firm-level decision using @Risk, a Monte Carlo computational tool.

The challenges and the limitations in Life Cycle Impact Assessment for metal oxide nanoparticles, a case study on nano- TiO2

Life Cycle Assessment (LCA) is a chain-oriented tool to evaluate the environment performance of products focussing on the entire life cycle of these products: from the extraction of resources, via manufacturing and use, to the final processing of the disposed products. Through all these stages consumption of resources and pollutant releases to air, water, soil are identified and quantified in Life Cycle Inventory (LCI) analysis. Subsequently to the LCI phase follows the Life Cycle Impact Assessment (LCIA) phase; that has the purpose to convert resource consumptions and pollutant releases in environmental impacts. The LCIA aims to model and to evaluate environmental issues, called impact categories. Several reports emphasises the importance of LCA in the field of ENMs. The ENMs offer enormous potential for the development of new products and application. There are however unanswered questions about the impacts of ENMs on human health and the environment. In the last decade the increasing production, use and consumption of nanoproducts, with a consequent release into the environment, has accentuated the obligation to ensure that potential risks are adequately understood to protect both human health and environment. Due to its holistic and comprehensive assessment, LCA is an essential tool evaluate, understand and manage the environmental and health effects of nanotechnology. The evaluation of health and environmental impacts of nanotechnologies, throughout the whole of their life-cycle by using LCA methodology. This is due to the lack of knowledge in relation to risk assessment. In fact, to date, the knowledge on human and environmental exposure to nanomaterials, such ENPs is limited. This bottleneck is reflected into LCA where characterisation models and consequently characterisation factors for ENPs are missed. The PhD project aims to assess limitations and challenges of the freshwater aquatic ecotoxicity potential evaluation in LCIA phase for ENPs and in particular nanoparticles as n-TiO2.

Investigation of heterogeneous catalysts for the synthesis of fine chemicals

In order to match the more stringent environmental regulations, heterogenization of traditional homogeneous processes is one of the main challenges of the modern chemical industry. Great results have been achieved in the fields of petrochemicals and base chemicals, whereas in fine chemical industry most of the synthetic procedures are based on multistep processes catalyzed by homogeneous catalysts mainly used in stoichiometric amounts. In the fine chemicals manufacture not so much efforts have been devoted to the investigation of suitable solid catalysts for the development of greener processes, then this sector represent a very attractive field of research. In this context, the present work deals with the extensive investigation of the possibility to heterogenize existing processes, in particular two different classes of reactions have been studied: alkylation of aromatic and heteroaromatic compounds and selective oxidation of aromatic alcohols. Traditional solid acid catalysts, such as zeolites, clays and alumina have been tested in the gas phase alkylation of 1,2-methylendioxybenzene, core building block of many drugs, pesticides and fragrances. The observed reactivity were clarified through a deep FTIR investigation complemented by ab initio calculation. The same catalysts were tested in the gas phase isopropylation of thiophene with the aim of clearly attribute the role of the reaction parameters in the reaction proceeding and verify the possibility to enhance the selectivity of one of the two possible isomers. Finally various Au/CeO2 catalysts were tested in the synthesis of benzaldehyde and piperonal, two aldehydes largely employed in the manufacture of fine chemical products, through liquid phase oxidation of the corresponding alcohols in very mild conditions.

Analysis of cumulative effects of multiple stressors on saltmarshes and consideration of management options

Natural systems face pressures exerted by natural physical-chemical forcings and a myriad of co-occurring human stressors that may interact to cause larger than expected effects, thereby presenting a challenge to ecosystem management. This thesis aimed to develop new information that can contribute to reduce the existing knowledge gaps hampering the holistic management of multiple stressors. I undertook a review of the state-of-the-art methods to detect, quantify and predict stressor interactions, identifying techniques that could be applied in this thesis research. Then, I conducted a systematic review of saltmarsh multiple stressor studies in conjunction with a multiple stressor mapping exercise for the study system in order to infer potential important synergistic stressor interactions. This analysis identified key stressors that are affecting the study system, but also pointed to data gaps in terms of driver and pressure data and raised issues for potentially overlooked stressors. Using field mesocosms, I explored how a local stressor (nutrient availability) affects the responses of saltmarsh vegetation to a global stressor (increased inundation) in different soil types. Results indicate that saltmarsh vegetation would be more drastically affected by increased inundation in low than in medium organic matter soils, and especially in estuaries already under high nutrient availability. In another field experiment, I examined the challenges of managing co-occurring and potentially interacting local stressors on saltmarsh vegetation: recreational trampling and smothering by deposition of excess macroalgal wrack due to high nutrient loads. Trampling and wrack prevention had interacting effects, causing non-linear responses of the vegetation to simulated management of these stressors, such that vegetation recovered only in those treatments simulating the combined prevention of both stressors. During this research I detected, using molecular genetic methods, a widespread presence of S. anglica (and to a lesser extent S. townsendii), two previously unrecorded non-native Spartinas in the study areas.

Deposition of thin films by a non-equilibrium atmospheric pressure Plasma Jet: a poly-diagnostic study

The use of atmospheric pressure plasmas for thin film deposition on thermo-sensitive materials is currently one of the main challenges of the plasma scientific community. Despite the growing interest in this field, the existing knowledge gap between gas-phase reaction mechanisms and thin film properties is still one of the most important barriers to overcome for a complete understanding of the process. In this work, thin films surface characterization techniques, combined with passive and active gas-phase diagnostic methods, were used to provide a comprehensive study of the Ar/TEOS deposition process assisted by an atmospheric pressure plasma jet. SiO2-based thin films exhibiting a well-defined chemistry, a good morphological structure and high uniformity were studied in detail by FTIR, XPS, AFM and SEM analysis. Furthermore, non-intrusive spectroscopy techniques (OES, filter imaging) and laser spectroscopic methods (Rayleigh scattering, LIF and TALIF) were employed to shed light on the complexity of gas-phase mechanisms involved in the deposition process and discuss the influence of TEOS admixture on gas temperature, electron density and spatial-temporal behaviours of active species. The poly-diagnostic approach proposed in this work opens interesting perspectives both in terms of process control and optimization of thin film performances.

Development of methodologies supporting the sustainable and safe integration of offshore conventional and renewable energy production

Against a backdrop of rapidly increasing worldwide population and growing energy demand, the development of renewable energy technologies has become of primary importance in the effort to reduce greenhouse gas emissions. However, it is often technically and economically infeasible to transport discontinuous renewable electricity for long distances to the shore. Another shortcoming of non-programmable renewable power is its integration into the onshore grid without affecting the dispatching process. On the other hand, the offshore oil & gas industry is striving to reduce overall carbon footprint from onsite power generators and limiting large expenses associated to carrying electricity from remote offshore facilities. Furthermore, the increased complexity and expansion towards challenging areas of offshore hydrocarbons operations call for higher attention to safety and environmental protection issues from major accident hazards. Innovative hybrid energy systems, as Power-to-Gas (P2G), Power-to-Liquid (P2L) and Gas-to-Power (G2P) options, implemented at offshore locations, would offer the opportunity to overcome challenges of both renewable and oil & gas sectors. This study aims at the development of systematic methodologies based on proper sustainability and safety performance indicators supporting the choice of P2G, P2L and G2P hybrid energy options for offshore green projects in early design phases. An in-depth analysis of the different offshore hybrid strategies was performed. The literature reviews on existing methods proposing metrics to assess sustainability of hybrid energy systems, inherent safety of process routes in conceptual design stage and environmental protection of installations from oil and chemical accidental spills were carried out. To fill the gaps, a suite of specific decision-making methodologies was developed, based on representative multi-criteria indicators addressing technical, economic, environmental and societal aspects of alternative options. A set of five case-studies was defined, covering different offshore scenarios of concern, to provide an assessment of the effectiveness and value of the developed tools.

Regulating Vertical Agreements: A Comparative Law & Economics analysis of Brazil and Europe

This PhD thesis discusses antitrust enforcement of anti-competitive vertical agreements in Europe and in Brazil from an institutional perspective. It considers both the evolution of the legal framework and the application of the existing policies, with the analysis of case studies. The research highlights the main challenges of the current approaches adopted by the competition authorities in these jurisdictions and formulates specific proposals for future improvements. Because the Brazilian competition rules were originally inspired by the European legal framework, this thesis also summarizes the contemporary discussions regarding comparative law and the efficiency of transplanting laws and good practices. In a Law & Economics perspective, vertical agreements have always been a paradoxical topic and constitute one of the most dynamic disputes for antitrust enforcement. The reason for that concern is the fact that those contracts among companies are complex in nature. Taking into account this background, the thesis provides an original analysis of the pro- and anti-competitive effects of vertical agreements, based on the classical literature of Law & Economics. One of the novelties of the research is the extension of the economic analysis of vertical agreements to also consider new forms of contractual abuses in the context of digital markets, such as the contractual restrictions that are being put I practice in e-commerce platforms. The international comparative approach focuses on the Brazilian and European experiences, and opens up a reflection about the policy recommendations applied to several countries with similar economic and institutional realities.

A conceptualisation of a governance model for biobanks in the digital society

Biobanks are key infrastructures in data-driven biomedical research. The counterpoint of this optimistic vision is the reality of biobank governance, which must address various ethical, legal and social issues, especially in terms of open consent, privacy and secondary uses which, if not sufficiently resolved, may undermine participants’ and society’s trust in biobanking. The effect of the digital paradigm on biomedical research has only accentuated these issues by adding new pressure for the data protection of biobank participants against the risks of covert discrimination, abuse of power against individuals and groups, and critical commercial uses. Moreover, the traditional research-ethics framework has been unable to keep pace with the transformative developments of the digital era, and has proven inadequate in protecting biobank participants and providing guidance for ethical practices. To this must be added the challenge of an increased tendency towards exploitation and the commercialisation of personal data in the field of biomedical research, which may undermine the altruistic and solidaristic values associated with biobank participation and risk losing alignment with societal interests in biobanking. My research critically analyses, from a bioethical perspective, the challenges and the goals of biobank governance in data-driven biomedical research in order to understand the conditions for the implementation of a governance model that can foster biomedical research and innovation, while ensuring adequate protection for biobank participants and an alignment of biobank procedures and policies with society’s interests and expectations. The main outcome is a conceptualisation of a socially-oriented and participatory model of biobanks by proposing a new ethical framework that relies on the principles of transparency, data protection and participation to tackle the key challenges of biobanks in the digital age and that is well-suited to foster these goals.

Development and characterization of deep learning techniques for neuroimaging data

Deep learning methods are extremely promising machine learning tools to analyze neuroimaging data. However, their potential use in clinical settings is limited because of the existing challenges of applying these methods to neuroimaging data. In this study, first a data leakage type caused by slice-level data split that is introduced during training and validation of a 2D CNN is surveyed and a quantitative assessment of the model’s performance overestimation is presented. Second, an interpretable, leakage-fee deep learning software written in a python language with a wide range of options has been developed to conduct both classification and regression analysis. The software was applied to the study of mild cognitive impairment (MCI) in patients with small vessel disease (SVD) using multi-parametric MRI data where the cognitive performance of 58 patients measured by five neuropsychological tests is predicted using a multi-input CNN model taking brain image and demographic data. Each of the cognitive test scores was predicted using different MRI-derived features. As MCI due to SVD has been hypothesized to be the effect of white matter damage, DTI-derived features MD and FA produced the best prediction outcome of the TMT-A score which is consistent with the existing literature. In a second study, an interpretable deep learning system aimed at 1) classifying Alzheimer disease and healthy subjects 2) examining the neural correlates of the disease that causes a cognitive decline in AD patients using CNN visualization tools and 3) highlighting the potential of interpretability techniques to capture a biased deep learning model is developed. Structural magnetic resonance imaging (MRI) data of 200 subjects was used by the proposed CNN model which was trained using a transfer learning-based approach producing a balanced accuracy of 71.6%. Brain regions in the frontal and parietal lobe showing the cerebral cortex atrophy were highlighted by the visualization tools.

Risk assessment to improve food safety along different food chains

The great challenges of today pose great pressure on the food chain to provide safe and nutritious food that meets regulations and consumer health standards. In this context, Risk Analysis is used to produce an estimate of the risks to human health and to identify and implement effective risk-control measures. The aims of this work were 1) describe how QRA is used to evaluate the risk for consumers health, 2) address the methodology to obtain models to apply in QMRA; 3) evaluate solutions to mitigate the risk. The application of a QCRA to the Italian milk industry enabled the assessment of Aflatoxin M1 exposure, impact on different population categories, and comparison of risk-mitigation strategies. The results highlighted the most sensitive population categories, and how more stringent sampling plans reduced risk. The application of a QMRA to Spanish fresh cheeses evidenced how the contamination of this product with Listeria monocytogenes may generate a risk for the consumers. Two risk-mitigation actions were evaluated, i.e. reducing shelf life and domestic refrigerator temperature, both resulting effective in reducing the risk of listeriosis. A description of the most applied protocols for data generation for predictive model development, was provided to increase transparency and reproducibility and to provide the means to better QMRA. The development of a linear regression model describing the fate of Salmonella spp. in Italian salami during the production process and HPP was described. Alkaline electrolyzed water was evaluated for its potential use to reduce microbial loads on working surfaces, with results showing its effectiveness. This work showed the relevance of QRA, of predictive microbiology, and of new technologies to ensure food safety on a more integrated way. Filling of data gaps, the development of better models and the inclusion of new risk-mitigation strategies may lead to improvements in the presented QRAs.

Methods and tools to foster territorial approaches for the assessment of food supply chains

The challenges of the current global food systems are often framed around feeding the world's growing population while meeting sustainable development for future generations. Globalization has brought to a fragmentation of food spaces, leading to a flexible and mutable supply chain. This poses a major challenge to food and nutrition security, affecting also rural-urban dynamics in territories. Furthermore, the recent crises have highlighted the vulnerability to shocks and disruptions of the food systems and the eco-system due to the intensive management of natural, human and economic capital. Hence, a sustainable and resilient transition of the food systems is required through a multi-faceted approach that tackles the causes of unsustainability and promotes sustainable practices at all levels of the food system. In this respect, a territorial approach becomes a relevant entry point of analysis for the food system’s multifunctionality and can support the evaluation of sustainability by quantifying impacts associated with quantitative methods and understanding the territorial responsibility of different actors with qualitative ones. Against this background the present research aims to i) investigate the environmental, costing and social indicators suitable for a scoring system able to measure the integrated sustainability performance of food initiatives within the City/Region territorial context; ii) develop a territorial assessment framework to measure sustainability impacts of agricultural systems; and iii) define an integrated methodology to match production and consumption at a territorial level to foster a long-term vision of short food supply chains. From a methodological perspective, the research proposes a mixed quantitative and qualitative research method. The outcomes provide an in-depth view into the environmental and socio-economic impacts of food systems at the territorial level, investigating possible indicators, frameworks, and business strategies to foster their future sustainable development.