Monitoring Complex Processes to Verify System Conformance: A Declarative Rule-Based Framework

Over the last 60 years, computers and software have favoured incredible advancements in every field. Nowadays, however, these systems are so complicated that it is difficult – if not challenging – to understand whether they meet some requirement or are able to show some desired behaviour or property. This dissertation introduces a Just-In-Time (JIT) a posteriori approach to perform the conformance check to identify any deviation from the desired behaviour as soon as possible, and possibly apply some corrections. The declarative framework that implements our approach – entirely developed on the promising open source forward-chaining Production Rule System (PRS) named Drools – consists of three components: 1. a monitoring module based on a novel, efficient implementation of Event Calculus (EC), 2. a general purpose hybrid reasoning module (the first of its genre) merging temporal, semantic, fuzzy and rule-based reasoning, 3. a logic formalism based on the concept of expectations introducing Event-Condition-Expectation rules (ECE-rules) to assess the global conformance of a system. The framework is also accompanied by an optional module that provides Probabilistic Inductive Logic Programming (PILP). By shifting the conformance check from after execution to just in time, this approach combines the advantages of many a posteriori and a priori methods proposed in literature. Quite remarkably, if the corrective actions are explicitly given, the reactive nature of this methodology allows to reconcile any deviations from the desired behaviour as soon as it is detected. In conclusion, the proposed methodology brings some advancements to solve the problem of the conformance checking, helping to fill the gap between humans and the increasingly complex technology.

A Bayesian changepoint analysis on spatio-temporal point processes

Changepoint analysis is a well established area of statistical research, but in the context of spatio-temporal point processes it is as yet relatively unexplored. Some substantial differences with regard to standard changepoint analysis have to be taken into account: firstly, at every time point the datum is an irregular pattern of points; secondly, in real situations issues of spatial dependence between points and temporal dependence within time segments raise. Our motivating example consists of data concerning the monitoring and recovery of radioactive particles from Sandside beach, North of Scotland; there have been two major changes in the equipment used to detect the particles, representing known potential changepoints in the number of retrieved particles. In addition, offshore particle retrieval campaigns are believed may reduce the particle intensity onshore with an unknown temporal lag; in this latter case, the problem concerns multiple unknown changepoints. We therefore propose a Bayesian approach for detecting multiple changepoints in the intensity function of a spatio-temporal point process, allowing for spatial and temporal dependence within segments. We use Log-Gaussian Cox Processes, a very flexible class of models suitable for environmental applications that can be implemented using integrated nested Laplace approximation (INLA), a computationally efficient alternative to Monte Carlo Markov Chain methods for approximating the posterior distribution of the parameters. Once the posterior curve is obtained, we propose a few methods for detecting significant change points. We present a simulation study, which consists in generating spatio-temporal point pattern series under several scenarios; the performance of the methods is assessed in terms of type I and II errors, detected changepoint locations and accuracy of the segment intensity estimates. We finally apply the above methods to the motivating dataset and find good and sensible results about the presence and quality of changes in the process.

Modelling, diagnostics and experimental analysis of plasma assisted processes for material treatment

This work presents results from experimental investigations of several different atmospheric pressure plasmas applications, such as Metal Inert Gas (MIG) welding and Plasma Arc Cutting (PAC) and Welding (PAW) sources, as well as Inductively Coupled Plasma (ICP) torches. The main diagnostic tool that has been used is High Speed Imaging (HSI), often assisted by Schlieren imaging to analyse non-visible phenomena. Furthermore, starting from thermo-fluid-dynamic models developed by the University of Bologna group, such plasma processes have been studied also with new advanced models, focusing for instance on the interaction between a melting metal wire and a plasma, or considering non-equilibrium phenomena for diagnostics of plasma arcs. Additionally, the experimental diagnostic tools that have been developed for industrial thermal plasmas have been used also for the characterization of innovative low temperature atmospheric pressure non equilibrium plasmas, such as dielectric barrier discharges (DBD) and Plasma Jets. These sources are controlled by few kV voltage pulses with pulse rise time of few nanoseconds to avoid the formation of a plasma arc, with interesting applications in surface functionalization of thermosensitive materials. In order to investigate also bio-medical applications of thermal plasma, a self-developed quenching device has been connected to an ICP torch. Such device has allowed inactivation of several kinds of bacteria spread on petri dishes, by keeping the substrate temperature lower than 40 degrees, which is a strict requirement in order to allow the treatment of living tissues.

Atmospheric plasma processes for microbial inactivation: food applications and stress response in Listeria monocytogenes

This PhD thesis is focused on cold atmospheric plasma treatments (GP) for microbial inactivation in food applications. In fact GP represents a promising emerging technology alternative to the traditional methods for the decontamination of foods. The objectives of this work were to evaluate: - the effects of GP treatments on microbial inactivation in model systems and in real foods; - the stress response in L. monocytogenes following exposure to different GP treatments. As far as the first aspect, inactivation curves were obtained for some target pathogens, i.e. Listeria monocytogenes and Escherichia coli, by exposing microbial cells to GP generated with two different DBD equipments and processing conditions (exposure time, material of the electrodes). Concerning food applications, the effects of different GP treatments on the inactivation of natural microflora and Listeria monocytogenes, Salmonella Enteritidis and Escherichia coli on the surface of Fuji apples, soya sprouts and black pepper were evaluated. In particular the efficacy of the exposure to gas plasma was assessed immediately after treatments and during storage. Moreover, also possible changes in quality parameters such as colour, pH, Aw, moisture content, oxidation, polyphenol-oxidase activity, antioxidant activity were investigated. Since the lack of knowledge of cell targets of GP may limit its application, the possible mechanism of action of GP was studied against 2 strains of Listeria monocytogenes by evaluating modifications in the fatty acids of the cytoplasmic membrane (through GC/MS analysis) and metabolites detected by SPME-GC/MS and 1H-NMR analyses. Moreover, changes induced by different treatments on the expression of selected genes related to general stress response, virulence or to the metabolism were detected with Reverse Transcription-qPCR. In collaboration with the Scripps Research Institute (La Jolla, CA, USA) also proteomic profiles following gas plasma exposure were analysed through Multidimensional Protein Identification Technology (MudPIT) to evaluate possible changes in metabolic processes.