Landslide activity in contrasting land-cover settings constrained by multi-temporal inventorying

This doctoral thesis focuses on the study of historical shallow landslide activity over time in response to anthropogenic forcing on land use, through the compilation of multi-temporal landslide inventories. The study areas, located in contrasting settings and characterized by different history of land-cover changes, include the Sillaro River basin (Italy) and the Tsitika and Eve River basins (coastal British Columbia). The Sillaro River basin belongs to clay-dominated settings, characterized by extensive badland development, and dominated by earth slides and earthflows. Here, forest removal began in the Roman period and has been followed by agricultural land abandonment and natural revegetation in recent time. By contrast, the Tsitika-Eve River basins are characterized by granitic and basaltic lithologies, and dominated by debris slides, debris flows and debris avalanches. In this setting, anthropogenic impacts started in 1960’s and have involved logging operation. The thesis begins with an introductory chapter, followed by a methodological section, where a multi-temporal mapping approach is proposed and tested at four landslide sites of the Sillaro River basin. Results, in terms of inventory completeness in time and space, are compared against the existing region-wide Emilia-Romagna inventory. This approach is then applied at the Sillaro River basin scale, where the multi-temporal inventory obtained is used to investigate the landslide activity in relation to historical land cover changes across geologic domains and in relation to hydro-meteorological forcing. Then, the impact of timber harvesting and road construction on landslide activity and sediment transfer in the Tsitika-Eve River basins is investigated, with a focus on the controls that interactions between landscape morphometry and cutblock location may have on landslide size-frequency relations. The thesis ends with a summary of the main findings and discusses advantages and limitations associated with the compilation of multi-temporal inventories in the two settings during different periods of human-driven, land-cover dynamics.

Multi-target-directed ligands: application to the Alzheimer's disease

The MTDL (multi-target-directed ligand) design strategy is used to develop single chemical entities that are able to simultaneously modulate multiple targets. The development of such compounds might disclose new avenues for the treatment of a variety of pathologies (e.g. cancer, AIDS, neurodegenerative diseases), for which an effective cure is urgently needed. This strategy has been successfully applied to Alzheimer’s disease (AD) due to its multifactorial nature, involving cholinergic dysfunction, amyloid aggregation, and oxidative stress. Despite many biological entities have been recognized as possible AD-relevant, only four achetylcholinesterase inhibitors (AChEIs) and one NMDA receptor antagonist are used in therapy. Unfortunately, such compounds are not disease-modifying agents behaving only as cognition enhancers. Therefore, MTDL strategy is emerging as a powerful drug design paradigm: pharmacophores of different drugs are combined in the same structure to afford hybrid molecules. In principle, each pharmacophore of these new drugs should retain the ability to interact with its specific site(s) on the target and, consequently, to produce specific pharmacological responses that, taken together, should slow or block the neurodegenerative process. To this end, the design and synthesis of several examples of MTDLs for combating neurodegenerative diseases have been published. This seems to be the more appropriate approach for addressing the complexity of AD and may provide new drugs for tackling the multifactorial nature of AD, and hopefully stopping its progression. According to this emerging strategy, in this work thesis different classes of new molecular structures, based on the MTDL approach, have been developed. Moreover, curcumin and its constrained analogs have currently received remarkable interest as they have a unique conjugated structure which shows a pleiotropic profile that we considered a suitable framework in developing MTDLs. In fact, beside the well-known direct antioxidant activity, curcumin displays a wide range of biological properties including anti-inflammatory and anti-amyloidogenic activities and an indirect antioxidant action through activation of the cytoprotective enzyme heme oxygenase (HO-1). Thus, since many lines of evidence suggest that oxidative stess and mitochondria impairment have a cental role in age-related neurodegenerative diseases such as AD, we designed mitochondria-targeted antioxidants by connecting curcumin analogs to different polyamine chains that, with the aid of electrostatic force, might drive the selected antioxidant moiety into mitochondria.

Use of shared memory in the context of embedded multi-core processors: exploration of the technology and its limits

Modern embedded systems embrace many-core shared-memory designs. Due to constrained power and area budgets, most of them feature software-managed scratchpad memories instead of data caches to increase the data locality. It is therefore programmers’ responsibility to explicitly manage the memory transfers, and this make programming these platform cumbersome. Moreover, complex modern applications must be adequately parallelized before they can the parallel potential of the platform into actual performance. To support this, programming languages were proposed, which work at a high level of abstraction, and rely on a runtime whose cost hinders performance, especially in embedded systems, where resources and power budget are constrained. This dissertation explores the applicability of the shared-memory paradigm on modern many-core systems, focusing on the ease-of-programming. It focuses on OpenMP, the de-facto standard for shared memory programming. In a first part, the cost of algorithms for synchronization and data partitioning are analyzed, and they are adapted to modern embedded many-cores. Then, the original design of an OpenMP runtime library is presented, which supports complex forms of parallelism such as multi-level and irregular parallelism. In the second part of the thesis, the focus is on heterogeneous systems, where hardware accelerators are coupled to (many-)cores to implement key functional kernels with orders-of-magnitude of speedup and energy efficiency compared to the “pure software” version. However, three main issues rise, namely i) platform design complexity, ii) architectural scalability and iii) programmability. To tackle them, a template for a generic hardware processing unit (HWPU) is proposed, which share the memory banks with cores, and the template for a scalable architecture is shown, which integrates them through the shared-memory system. Then, a full software stack and toolchain are developed to support platform design and to let programmers exploiting the accelerators of the platform. The OpenMP frontend is extended to interact with it.

Applying machine learning: a multi-role perspective

Machine (and deep) learning technologies are more and more present in several fields. It is undeniable that many aspects of our society are empowered by such technologies: web searches, content filtering on social networks, recommendations on e-commerce websites, mobile applications, etc., in addition to academic research. Moreover, mobile devices and internet sites, e.g., social networks, support the collection and sharing of information in real time. The pervasive deployment of the aforementioned technological instruments, both hardware and software, has led to the production of huge amounts of data. Such data has become more and more unmanageable, posing challenges to conventional computing platforms, and paving the way to the development and widespread use of the machine and deep learning. Nevertheless, machine learning is not only a technology. Given a task, machine learning is a way of proceeding (a way of thinking), and as such can be approached from different perspectives (points of view). This, in particular, will be the focus of this research. The entire work concentrates on machine learning, starting from different sources of data, e.g., signals and images, applied to different domains, e.g., Sport Science and Social History, and analyzed from different perspectives: from a non-data scientist point of view through tools and platforms; setting a problem stage from scratch; implementing an effective application for classification tasks; improving user interface experience through Data Visualization and eXtended Reality. In essence, not only in a quantitative task, not only in a scientific environment, and not only from a data-scientist perspective, machine (and deep) learning can do the difference.