Influence of alternative electron acceptors on the anaerobic biodegradation of polycyclic aromatic hydrocarbons

Polycyclic aromatic hydrocarbons are chemicals produced by both human activities and natural sources and they have been present in the biosphere since millions of years. For this reason microorganisms should have developed, during the world history, the capacity of metabolized them under different electron acceptors and redox conditions. The deep understanding of these natural attenuation processes and of microbial degradation pathways has a main importance in the cleanup of contaminated areas. Anaerobic degradation of aromatic hydrocarbons is often presumed to be slow and of a minor ecological significance compared with the aerobic processes; however anaerobic bioremediation may play a key role in the transformation of organic pollutants when oxygen demand exceeds supply in natural environments. Under such conditions, anoxic and anaerobic degradation mediated by denitrifying or sulphate-reducing bacteria can become a key pathway for the contaminated lands clean up. Actually not much is known about anaerobic bioremediation processes. Anaerobic biodegrading techniques may be really interesting for the future, because they give the possibility of treating contaminated soil directly in their natural status, decreasing the costs concerning the oxygen supply, which usually are the highest ones, and about soil excavations and transports in appropriate sites for a further disposal. The aim of this dissertation work is to characterize the conditions favouring the anaerobic degradation of polycyclic aromatic hydrocarbons. Special focus will be given to the assessment of the various AEA efficiency, the characterization of degradation performance and rates under different redox conditions as well as toxicity monitoring. A comparison with aerobic and anaerobic degradation concerning the same contaminated material is also made to estimate the different biodegradation times.

Studies of CMS data access patterns with machine learning techniques

This thesis presents a study of the Grid data access patterns in distributed analysis in the CMS experiment at the LHC accelerator. This study ranges from the deep analysis of the historical patterns of access to the most relevant data types in CMS, to the exploitation of a supervised Machine Learning classification system to set-up a machinery able to eventually predict future data access patterns - i.e. the so-called dataset “popularity” of the CMS datasets on the Grid - with focus on specific data types. All the CMS workflows run on the Worldwide LHC Computing Grid (WCG) computing centers (Tiers), and in particular the distributed analysis systems sustains hundreds of users and applications submitted every day. These applications (or “jobs”) access different data types hosted on disk storage systems at a large set of WLCG Tiers. The detailed study of how this data is accessed, in terms of data types, hosting Tiers, and different time periods, allows to gain precious insight on storage occupancy over time and different access patterns, and ultimately to extract suggested actions based on this information (e.g. targetted disk clean-up and/or data replication). In this sense, the application of Machine Learning techniques allows to learn from past data and to gain predictability potential for the future CMS data access patterns. Chapter 1 provides an introduction to High Energy Physics at the LHC. Chapter 2 describes the CMS Computing Model, with special focus on the data management sector, also discussing the concept of dataset popularity. Chapter 3 describes the study of CMS data access patterns with different depth levels. Chapter 4 offers a brief introduction to basic machine learning concepts and gives an introduction to its application in CMS and discuss the results obtained by using this approach in the context of this thesis.