RNA binding proteins (RBPs) and non-coding RNAs (ncRNAs) are key actors in post-transcriptional gene regulation. By being able to bind messenger RNA (mRNA) they modulate many regulatory processes. In the last years, the increasing interest in this level of regulation favored the development of many NGS-based experimental techniques to detect RNA-protein interactions, and the consequent release of a considerable amount of interaction data on a growing number of eukaryotic RBPs. Despite the continuous advances in the experimental procedures, these techniques are still far from fully uncovering, on their own, the global RNA-protein interaction system. For instance, the available interaction data still covers a small fraction (less than 10%) of the known human RBPs. Moreover, experimentally determined interactions are often noisy and cell-line dependent. Importantly, obtaining genome-wide experimental evidence of combinatorial interactions of RBPs is still an experimental challenge. Machine learning approaches are able to learn from the data and generalize the information contained in them. This might give useful insights to help the investigation of the post-transcriptional regulation. In this work, three machine learning contributions are proposed. They aim at addressing the three above-mentioned shortcomings of the experimental techniques, to help researchers unveiling some yet uncharacterized aspects of post-transcriptional gene regulation. The first contribution is RNAcommender, a tool capable of suggesting RNA targets to unexplored RBPs at a genome-wide level. RNAcommender is a recommender system that propagates the available interaction data, considering biologically relevant aspects of the RNA-protein interactions, such as protein domains and RNA predicted secondary structure. The second contribution is ProtScan, a tool that models RNA-protein interactions at a single-nucleotide resolution. Learning models from experimentally determined interactions allows to denoise the data and to make predictions of the RBP binding preferences in conditions that are different from those of the experiment. The third and last contribution is PTRcombiner, a tool that unveils the combinatorial aspects of post-transcriptional gene regulation. It extracts clusters of mRNA co-regulators from the interaction annotations, and it automatically provides a biological analysis that might supply a functional characterization of the set of mRNAs targeted by a cluster of co-regulators, as well as of the binding dynamics of different RBPs belonging to the same cluster.

Modeling business processes is a complex and time-consuming task, which can be simplified by allowing process instances to be structurally adapted at runtime, based on context (e.g., by adding or deleting activities). The process model then no longer needs to include a handling procedure for every exception that can occur. Instead, it only needs to include the assumptions under which a successful execution is guaranteed. If a design-time assumption is violated, the exception handling procedure matching the context is selected at runtime. However, if runtime structural adaptation is allowed, the process model may later need to be updated based on the logs of adapted process instances. Evolving the process model is necessary if adapting at run-time is too costly, or if certain adaptations fail and should be avoided. An issue that is insufficiently addressed in the previous work on process evolution is how to evolve a process model and also ensure that the evolved process model continues to achieve the goal of the original model. We refer to the problem of evolving a process model based on selected instance adaptations, such that the evolved model satisfies the goal of the original model, as corrective evolution. Automated techniques for solving the corrective evolution problem are necessary for two reasons. First, the more complex a process model is, the more difficult it is to be changed manually. Second, there is a need to verify that the evolved model satisfies the original goal. To develop automated techniques, we first formalize the problem of corrective evolution. Since we use a graph-based representation of processes, a key element in our formal model is the notion of trace. When plugging an instance adaptation at a particular point in the process model, there can be multiple paths in the model for reaching this point. Each of these paths is uniquely identified by a trace, i.e., a recording of the activities executed up to that point. Depending on traces, an instance adaptation can be used to correct the process model in three different ways. A correction is strict if the adaptation should be plugged in on a precise trace, relaxed if on all traces, and relaxed with conditions if on a subset of all traces. The choice is driven by competing concerns: the evolved model should not introduce untested behavior, but it should also remain understandable. Using our formal model, we develop automated techniques for solving the corrective evolution problem in two cases. The first case is also the most restrictive, when all corrections are strict. This case does not require verification, since the process model and adaptations are assumed to satisfy the goal, as long as the adaptations are applied on the corresponding traces. The second case is when corrections are either strict or relaxed. This second case requires verification, and for this reason we develop an automated technique based on planning. We implemented the two automated techniques as tools, which are integrated into a common toolkit. We used this toolkit to evaluate the tradeoffs between applying strict and relaxed corrections on a scenario built on a real event log.

The contemporary cities are facing affected by three factors that are changing our lifestyle: the economic and relative social crisis getting worse by the pandemic, the technological revolution and the climate changes effects. In this framework, this thesis investigates the adaptive urban planning as a part of DICAM - Trento Urban Transformation Research Programme, which started in 2017 to provide scientific support for the Trento general urban plan review. This doctoral research aims to demonstrate how the open, adaptive and metabolic plan can respond to city demands by means of Collaborative urban Transformations: the processes that go beyond the dichotomous relationship between the strategical approach and the tactical one. The thesis output is an Open Toolbox made of strategies, tactics and devices to catalyses the challenges, goals and actions of adaptive urban plan, as the Trento Leaf Plan proposed by the TUT research group. The final Manifesto has been proposed to test and implement in other contexts the new planning approach capable to activating the ecological transition, as an adaptive, multi-scalar and interdisciplinary process that leads towards a city more ECO, ACCOGLIENTE, ACCESSIBILE, SMART and BELLA.

Contact management is a twofold problem involving a local and global level where the separation between them is rather fuzzy. Locally, users need to deal with contact management, which refers to a local need to store, organize, maintain up to date, and find information that will allow them contacting or reaching other people, organizations, etc. Globally, users deal with identity management that refers to peers having multiple identities (i.e., profiles) and the need of staying in control of them. In other words, they should be able to manage what information is shared and with whom. We believe many existing applications try to deal with this problem looking only at the data level and without analyzing the underlying complexity. Our approach focus on the complex social relations and interactions between users, identifying three main subproblem: (i) management of identity, (ii) search, and (iii) privacy. The solution we propose concentrates on the models that are needed to address these problems. In particular, we propose a Distributed Contact Management System (DCM System) that: Models and represents the knowledge of peers about physical or abstract objects through the notion of entities that can be of different types (e.g., locations, people, events, facilities, organizations, etc.) and are described by a set of attributes; By representing contacts as entities, allows peers to locally organize their contacts taking into consideration the semantics of the contact’s characteristics; By describing peers as entities allows them to manage their different identities in the network, by sharing different views of themselves (showing possibly different in- formation) with different people. The contributions of this thesis are, (i) the definition of a reference architecture that allows dealing with the diversity in relation with the partial view that peers have of the world, (ii) an approach to search entities based on identifiers, (iii) an approach to search entities based on descriptions, and (iv) the definition of the DCM system that instantiates the previously mentioned approaches and architecture to address concrete usage scenarios.

Climate change is directly or indirectly affecting cities, regions or even nations in multiple ways. Impacts are exponential and repetitive with increased instability of climate pattern, socio-ecological systems, increased inequalities and distribution of resources. It is therefore necessary that social and economic hubs and potential resource rich region should become the catalyst that encourages the focus on climate change policies. Despite having various international and national climate change frameworks and forums it is unclear how international, national and even local governments develop response actions to climate concerns and integrate them into different spatial scales. Developing and mainstreaming effective response actions to climate change into numerous sectors, cross-sectoral policies is a complex issue which has plagued policy makers at different spatial scales and on different policy arenas. In order to efficiently integrate and sensitizing society towards climate change issues, decision makers and different stakeholders have to develop insightful information bases, share awareness of climate change risks, vulnerability patterns and finally develop response actions at all level of policy preparation through policy integration, implementation or structural reforms. This study contributes towards understanding climate change risks and perception within spatial planning policies at local level. This has been undertaken by investigating, testing or developing real spatial planning policies, vulnerability assessment frameworks and decision support systems that aim to improve current spatial planning tools intended at building climate resilient living spaces. This study was divided into three main stages 1) To develop and test an assessment framework to track integration of climate change issues into spatial planning, 2) To identify hot spots of climate change at urban/regional levels by applying spatial vulnerability assessment tools and 3) To apply eco-system based adaption responses to climate change in an urban region and identifying barriers. Drawing the case study from India, in the first stage, an attempt was made to understand how spatial plans in India are incorporating climate change issues and identifying potential gaps. Spatial plans across various cities in India were examined with the help of a review framework that was developed upon Moser and Loer’s (2008) work on ''Managing climate risks''. The second stage presents a climate change vulnerability assessment framework and its working methodology at local spatial scale, considering three main components: exposure, sensitivity and adaptive capacity. The vulnerability assessment framework was applied to an urban area in India, namely, Bangalore and a hill district of Eastern Himalaya namely Darjeeling. In the final stage of this study, ecosystem services based adaptation responses within spatial planning was studies to understand how it can increase adaptive capacity and address climate changes issues. The results of this study identified key concerns to climate change issues and its integration in India. The policy analysis shows that the role of spatial plans to integrate climate change issues at local levels like urban areas and regions in India are still limited. Local policies and spatial plans shows low level of awareness, moderate level of analytical capability and limited action responses to integrate climate change issues at local level. Spatial policies in India are still limited to physical and economic issues and undermine the issues of climate change. The application of vulnerability assessment framework demonstrated that it successfully provided a spatial assessment of climate change vulnerability patterns. The spatial pattern of vulnerability identifies areas requiring urgent attention to adaptation action, enabling policy intervention and prioritizing action. At the same time an analysis of the perception of people also confirmed the results of vulnerability assessment at local level. Finally the results showed how ecosystem services based response actions when applied within spatial planning can play an important role to mitigate the effects of climate change and adapt to local climate concerns with least negative repercussions. The findings of this study creates a platform for discussion on decision making process and the potential aspects where climate change issues can become a part of spatial planning policy. Climate change mitigation and adaptation for short terms may fulfill objectives for current climate scenarios but may impose externalities in future. So, policy makers and local development organization need to carefully narrate future climate resilient scenarios. This study is the reflection of the interrelationship between the existing information bases, knowledge gaps, policy preparation practices, analytical capability, participation and technological innovation in climate change integration at local spatial scale.

Alpine rivers have been regulated to claim productive land in valley bottoms since the last two centuries. Width reduction and rectification often induced the development of regular scour-deposition sequences, called alternate bars, with implications for flood protection, river navigation, environmental integrity. Understanding how alternate bars evolves in rivers and defining the key aspects that influence the development of these regular deposits of sediments represents a challenge that is not fully described. Most studies on alternate bars are in fact based on mathematical theories, laboratory experiments and recently numerical simulations, but only few studies on field cases have been performed so far. The goals of this work are: i) to quantify the morphodynamics of alternate bars in the Alpine Rhine River, with a particular emphasis on bar migration; ii) to assess to what extent the predictions of analytical bar theories are consistent with field observations and to explore how theories may help interpret observed alternate bars dynamics; iii) to determine the ability of a numerical model to simulate correctly the formation and the length scale of alternate bars and influence of different multi-decadal inflow conditions. The 42-km chosen reach is located along the border between Austria and Switzerland, between the confluences of Landquart and Ill rivers. The whole reach has been completely embanked starting from the 19th century, so alternate bars are present for more than a century. Moreover the simplification of the cross section, together with the presence of only few bends, puts the Alpine Rhine in the ideal position to be compared with analytical theories of alternate bars in straight channels. The goals are achieved by analyzing a dataset of freely available Landsat imagery, which combine unprecedented temporal length (3 decades), spatial length (more than 400 channel widths) and temporal resolution (around 3 images per year). Bars show a spatially selective behavior, with short, bars occurring in distinct straight reaches with respect to longer bars. The same evidence is found in terms of bar migration, so that short bars are shown to migrate more than longer bars, in agreement with theoretical predictions. A full range of bar wavelengths and more complex patterns occur in reaches with bends and ramps. Bar height, obtained from cross section monitoring, was found to be much more uniform. The temporally long dataset, including approximately 30 floods with different magnitude and duration, allowed the investigation of bar migration as a function of discharge, showing that bars migrate faster for intermediate foods. Predicted values of linear theories for free and forced bars in straight channels are in good general agreement with field observations, when considering conditions of bar formation and bar wavelength. Comparing theories and observations suggests that theoretical outcomes may represent the boundaries of the actual, wide range of bars’ behaviour, which likely reflects non-linear interactions, flow unsteadiness, sediment size hetero- geneity and finite length of straight reaches, which are not retained in linear theories. Non-linear interactions are investigated through the 2D numerical morphodynamic model Basement, developed at the Swiss Federal Institute of Technology of Zurich. Preliminary investigations focus on the role of the transversal sediment transport, that behaves as a diffusive term. The numerical diffusion can be indirectly evaluated starting from the calibration of the coefficient of the diffusive term and a benchmark methodology to evaluate the lateral and numerical diffusion is defined. The results are used in the morphological calibration of the model. The spatial trend of wavelengths is in general agreement with the field data, and the migration take place mainly in correspondence of short bars, while long bars tend to elongate with time. The choice of a constant discharge or a real hydrograph influences the time scale of bar evolution. The present analysis results in the longest spatial and temporal field case study of river bars in channelized rivers with a temporal survey resolution that allows the investigation of the effect of individual flood events, and provides new quantitative data on bar wavelength and migration. The dataset provides useful information to assess the applicability of analytical bar theories, so far mainly tested against flume experiments, and following recent attempts in French and Dutch streams. Moreover, a novel two-dimensional morphological benchmark to access the role of numerical diffusion is proposed. The new insights are crucial to design future management scenarios accounting for hydraulic safety and environmental quality.

In recent years, the automatic analysis of human behaviour has been attracting an increasing amount of attention from researchers because of its important applicative aspects and its intrinsic scientific interest. In many technological fields (pervasive and ubiquitous computing, multimodal interaction, ambient as-sisted living and assisted cognition, computer supported collaborative work, user modelling, automatic visual surveillance, etc.) the awareness is emerging that system can provide better and more appropriate services to people only if they can understand much more of what they presently do about users’ attitudes, preferences, personality, etc., as well as about what people are doing, the activities they have been en-gaged in the past, etc. At the same time, progress on sensors, sensor networking, computer vision, audio analysis and speech recognition are making available the building blocks for the automatic behavioural analysis. Multimodal analysis—the joint consideration of several perceptual channels—is a powerful tool to extract large and varied amounts of information from the acoustical and visual scene and from other sensing devices (e.g., RFIDs, on-body accelerometers, etc.). In this thesis, we consider small group meetings as a challenging example and case study of real life situations in which the multimodal analysis of social signals can be used to extract relevant information about the group and about individuals. In particular, we show how the same type of social signals can be used to reconstruct apparently disparate and diverse aspects of social and individual life ranging from the functional roles played by the participants in a meeting, to static characteristics of individuals (per-sonality traits) and behavioural outcomes (task performance).

Semantics is a local and a global problem at the same time. Local because is in the mind of the people who have personal interpretations, and global because we need to reach a common understanding by sharing and aligning these personal interpretations. As opposed to current state-of-the-art approaches based on a two layer architecture (local and global), we deal with this problem by designing a general three layer architecture rooted on the personal, social, and universal levels. The new intermediate social level acts as a global level for the personal level, where semantics is managed around communities focusing on specific domains, and as local for the universal level as it only deals with one part of universal knowledge. For any of these layers there are three main components of knowledge that helps us encode the semantics at the right granularity. These are: i) Concrete knowledge, which allows us to achieve semantic compatibility at the level of entities, the things we want to talk about; ii) Schematic knowledge, which defines the structure and methods of the entities; and iii) Background knowledge, which enables compatibility at the language level used to describe and structure entities. The contribution of this work is threefold: i) the definition of general architecture for managing semantics of entities, ii) the development components of the system based on the architecture; these are structure preserving semantic matching and sense induction algorithms, and iii) the evaluation of these components with the creation of new gold standards datasets.

With the raising concerns about the environment, the ICT equipments have been pointed out as a major and ever rising source of energy consumption and pollution. Among those ICT equipments, data centres play obviously a major role with the rise of the Cloud computing paradigm. In the recent years, researchers have focused on reducing the energy consumption of data centres. Furthermore, future environmentally friendly data centres are also expected to prioritize the usage of renewable energies over brown energies. However, managing the energy consumption within a data centre is challenging because data centres are complex facilities which supports a huge variety of hardware, computing styles and SLAs. Those may evolve through time as user requirements can change rapidly. Furthermore, differently from non-renewable energy sources, the availability of renewable energies is very volatile and time dependent: e.g. solar power is obtainable only during the day, and is subject to variations due to the meteorological conditions. The goal in this case is to shift the workload of running applications, according to the forecasted availability of the renewable energy. In this thesis we propose a flexible framework called Plug4Green able to reduce the energy consumption of a Cloud data centre. Plug4Green is based on the Constraint Programming paradigm, allowing it to take into account a great number of constraints regarding energy, hardware and SLAs in data centres. We also propose the concept of an energy adaptive software controller (EASC), able to augment the usage of renewable energies in data centres. The EASC supports two kind of applications: service-oriented and task-oriented applications; and two kind of computing environments: Infrastructure as a Service and Platform as a Service. We evaluated our solutions in several trials executed in the testbeds of Milan and Trento, Italy. Results show that Plug4Green was able to reduce the power consumption by 27% in the Milan trial, while the EASC was able to augment the renewable energy percentage by 7.07pp in the Trento trial.

Alpine rivers have been regulated to claim productive land in valley bottoms since the last two centuries. Width reduction and rectification often induced the development of regular scour-deposition sequences, called alternate bars, with implications for flood protection, river navigation, environmental integrity. Understanding how alternate bars evolve in rivers and defining the key aspects that influence the development of these regular deposits of sediments represents a challenge that is not fully described. Most studies on alternate bars are in fact based on mathematical theories, laboratory experiments and since 1990s numerical simulations, but only few studies on field cases have been performed so far. The goals of this work are: i) to quantify the morphodynamics of alternate bars in the Alpine Rhine River, with a particular emphasis on bar migration; ii) to assess to what extent the predictions of analytical bar theories are consistent with field observations and to explore how theories may help interpret observed alternate bars dynamics; iii) to determine the ability of a numerical model to simulate correctly the formation and the length scale of alternate bars and the influence of different multi-decadal inflow conditions. The 42 km chosen reach is located along the border between Austria and Switzerland, between the confluences of Landquart and Ill rivers. The whole reach has been completely embanked starting from the 19th century, so alternate bars have been present for more than a century. Moreover the simplification of the cross section, together with the presence of only few bends, puts the Alpine Rhine in the ideal position to be compared with analytical theories of alternate bars in straight channels. The goals are achieved by analyzing a dataset of freely available Landsat imagery, which combine unprecedented temporal length (3 decades), spatial length (more than 400 channel widths) and temporal resolution (around 3 images per year). Bars show a spatially selective behavior, with short bars occurring in distinct straight reaches with respect to longer bars. The same evidence is found in terms of bar migration, so that short bars are shown to migrate more than longer bars, in agreement with theoretical predictions. A full range of bar wavelengths and more complex patterns occur in reaches with bends and ramps. Bar height, obtained from cross section monitoring, was found to be much more uniform. The temporally long dataset, including approximately 30 floods with different magnitude and duration, allowed the investigation of bar migration as a function of discharge, showing that bars migrate faster for intermediate floods. Predicted values of linear theories for free and forced bars in straight channels are in good general agreement with field observations, when considering conditions of bar formation and bar wavelength. Comparing theories and observations suggests that theoretical outcomes may represent the boundaries of the actual, wide range of bar behavior, which likely reflects non-linear interactions, flow unsteadiness, sediment size heterogeneity and finite length of straight reaches, which are not retained in linear theories. Non-linear interactions are investigated through the 2D numerical morphodynamic model Basement, developed at the Swiss Federal Institute of Technology of Zurich. Preliminary investigations focus on the role of the transverse sediment transport, that behaves as a diffusive term. The numerical diffusion can be indirectly evaluated starting from the calibration of the coefficient of the diffusive term. A benchmark methodology to evaluate the lateral and numerical diffusion is defined. The results are used in the morphological calibration of the model. The spatial trend of wavelengths is in general agreement with the field data, and the migration takes place mainly in correspondence to short bars, whereas long bars tend to elongate with time. The choice of a constant discharge or a real hydrograph influences the time scale of bar evolution. The present analysis results in the longest spatial and temporal field case study of river bars in channelized rivers with a temporal survey resolution that allows the investigation of the effect of individual flood events, and provides new quantitative data on bar wavelength and migration. The dataset provides useful information to assess the applicability of analytical bar theories, so far mainly tested against flume experiments, and following recent attempts in French and Dutch streams. Moreover, a novel two-dimensional morphological benchmark to access the role of numerical diffusion is proposed.