Adaptive edge analytics for distributed networked control of water systems

Over the last decade, there has been a trend where water utility companies aim to make water distribution networks more intelligent in order to improve their quality of service, reduce water waste, minimize maintenance costs etc., by incorporating IoT technologies. Current state of the art solutions use expensive power hungry deployments to monitor and transmit water network states periodically in order to detect anomalous behaviors such as water leakage and bursts. However, more than 97% of water network assets are remote away from power and are often in geographically remote underpopulated areas, facts that make current approaches unsuitable for next generation more dynamic adaptive water networks. Battery-driven wireless sensor/actuator based solutions are theoretically the perfect choice to support next generation water distribution. In this paper, we present an end-to-end water leak localization system, which exploits edge processing and enables the use of battery-driven sensor nodes. Our system combines a lightweight edge anomaly detection algorithm based on compression rates and an efficient localization algorithm based on graph theory. The edge anomaly detection and localization elements of the systems produce a timely and accurate localization result and reduce the communication by 99% compared to the traditional periodic communication. We evaluated our schemes by deploying non-intrusive sensors measuring vibrational data on a real-world water test rig that have had controlled leakage and burst scenarios implemented.

IMPROVED SAMPLE INTRODUCTION SYSTEMS FOR INDUCTIVELY COUPLED PLASMA OPTICAL EMISSION SPECTROMETRY AND THEIR APPLICATION TO THE ANALYSIS OF SIMPLE AND COMPLEX MATRICES

The objective of this thesis is to explore new and improved methods for greater sample introduction efficiency and enhanced analytical performance with inductively coupled plasma optical emission spectrometry (ICP-OES). Three projects are discussed in which the capabilities and applications of ICP-OES are expanded: 1. In the first project, a conventional ultrasonic nebuliser was modified to replace the heater/condenser with an infrared heated pre-evaporation tube. In continuation from previous works with pre-evaporation, the current work investigated the effects of heating with infrared block and rope heaters on two different ICP-OES instruments. Comparisons were made between several methods and setups in which temperatures were varied. By monitoring changes to sensitivity, detection limit, precision, and robustness, and analyzing two certified reference materials, a method with improved sample introduction efficiency and comparable analytical performance to a previous method was established. 2. The second project involved improvements to a previous work in which a multimode sample introduction system (MSIS) was modified by inserting a pre-evaporation tube between the MSIS and torch. The new work focused on applying an infrared heated ceramic rope for pre-evaporation. This research was conducted in all three MSIS modes (nebulisation mode, hydride generation mode, and dual mode) and on two different ICP-OES instruments, and comparisons were made between conventional setups in terms of sensitivity, detection limit, precision, and robustness. By tracking both hydride-forming and non-hydride forming elements, the effects of heating in combination with hydride generation were probed. Finally, optimal methods were validated by analysis of two certified reference materials. 3. A final project was completed in collaboration with ZincNyx Energy Solutions. This project sought to develop a method for the overall analysis of a 12 M KOH zincate fuel, which is used in green energy backup systems. By employing various techniques including flow injection analysis and standard additions, a final procedure was formulated for the verification of K concentration, as well as the measurement of additives (Al, Fe, Mg, In, Si), corrosion products (such C from CO₃²¯), and Zn particles both in and filtered from solution. Furthermore, the effects of exposing the potassium zincate electrolyte fuel to air were assessed.

A novel CE microchip with micro pillars column & double-L injection design for Capacitance Coupled Contactless Conductivity detection technology

This novel capillary electrophoresis microchip, or also known as μTAS (micro total analysis system) was designed to separate complex aqueous based compounds, similar to commercial CE & microchip (capillary electrophoresis) systems, but more compact. This system can be potentially used for mobile/portable chemical analysis equipment. Un-doped silicon wafer & ultra-thin borofloat glass (Pyrex) wafers have been used to fabricate the device. Double-L injection feature, micro pillars column, bypass separation channel & hybrid- referenced C4D electrodes were designed to achieve a high SNR (signal to noise ratio), easy- separation, for a durable and reusable μTAS for CE use.

Screening a Case Base for Stroke Disease Detection

Stroke stands for one of the most frequent causes of death, without distinguishing age or genders. Despite representing an expressive mortality fig-ure, the disease also causes long-term disabilities with a huge recovery time, which goes in parallel with costs. However, stroke and health diseases may also be prevented considering illness evidence. Therefore, the present work will start with the development of a decision support system to assess stroke risk, centered on a formal framework based on Logic Programming for knowledge rep-resentation and reasoning, complemented with a Case Based Reasoning (CBR) approach to computing. Indeed, and in order to target practically the CBR cycle, a normalization and an optimization phases were introduced, and clustering methods were used, then reducing the search space and enhancing the cases re-trieval one. On the other hand, and aiming at an improvement of the CBR theo-retical basis, the predicates` attributes were normalized to the interval 0…1, and the extensions of the predicates that match the universe of discourse were re-written, and set not only in terms of an evaluation of its Quality-of-Information (QoI), but also in terms of an assessment of a Degree-of-Confidence (DoC), a measure of one`s confidence that they fit into a given interval, taking into account their domains, i.e., each predicate attribute will be given in terms of a pair (QoI, DoC), a simple and elegant way to represent data or knowledge of the type incomplete, self-contradictory, or even unknown.

A Case Based Methodology for Problem Solving aiming at Knee Osteoarthritis Detection

Knee osteoarthritis is the most common type of arthritis and a major cause of impaired mobility and disability for the ageing populations. Therefore, due to the increasing prevalence of the malady, it is expected that clinical and scientific practices had to be set in order to detect the problem in its early stages. Thus, this work will be focused on the improvement of methodologies for problem solving aiming at the development of Artificial Intelligence based decision support system to detect knee osteoarthritis. The framework is built on top of a Logic Programming approach to Knowledge Representation and Reasoning, complemented with a Case Based approach to computing that caters for the handling of incomplete, unknown, or even self-contradictory information.

Personalization of Electronic Nicotine Delivery Systems Toxicological impact in in-vivo model

Electronic nicotine delivery systems (ENDS) use has recently grown. E-cig generates carcinogenic chemical compounds and reactive oxygen species (ROS). Carbonyls and ROS are formed when the liquid comes into contact with the heating element. In this study the chemical and biological effects of coil resistance applied on the same device were investigated. A preliminary in-vivo study the new heat-not-burn devices (IQOS®) has been conducted to evaluate the effect of the device on antioxidant biomarkers. The amount of formaldehyde, acetaldehyde, acrolein was measured by GC-MS analysis. The two e-liquids used for carbonyls detection differed only for the presence of nicotine. The nicotine-free liquid was then used for the detection of ROS in the aerosol. The impact of the non-nicotine vapor on cell viability in H1299 human lung carcinoma cells, as well as the biological effects in a rat model of e-cig aerosol exposure, were also evaluated. After the exposure of Sprague Dawley rats to e-cig and IQOS® aerosol, the effect of 28-day treatment was examined on enzymatic and non-enzymatic antioxidant response, lung inflammation, blood homeostasis and tissue damage by using scanning electron microscope (SEM) technique. The results show a significant correlation between the low resistance and the generation of higher concentrations of the selected carbonyls and ROS in aerosols. Cell viability was reduced with an inverse relation to coil resistance. The experimental model highlighted an impairment of the pulmonary antioxidant and detoxifying machinery. Frames from SEM show disorganization of alveolar and bronchial epithelium. IQOS® exposed animals shows a significant production of ROS related to the unbalance of antioxidant defense and alteration of macromolecule integrity. This research demonstrates how several toxicological aspects can potentially occur in e-cig consumers who use low resistance device coupled with nicotine-free liquid. ENDS may expose users to hazardous compounds, which, may promote chronic pathologies and degenerative diseases.

Monitoring and Prediction of Thermal Emergencies in High Performance Computing Systems

Modern scientific discoveries are driven by an unsatisfiable demand for computational resources. High-Performance Computing (HPC) systems are an aggregation of computing power to deliver considerably higher performance than one typical desktop computer can provide, to solve large problems in science, engineering, or business. An HPC room in the datacenter is a complex controlled environment that hosts thousands of computing nodes that consume electrical power in the range of megawatts, which gets completely transformed into heat. Although a datacenter contains sophisticated cooling systems, our studies indicate quantitative evidence of thermal bottlenecks in real-life production workload, showing the presence of significant spatial and temporal thermal and power heterogeneity. Therefore minor thermal issues/anomalies can potentially start a chain of events that leads to an unbalance between the amount of heat generated by the computing nodes and the heat removed by the cooling system originating thermal hazards. Although thermal anomalies are rare events, anomaly detection/prediction in time is vital to avoid IT and facility equipment damage and outage of the datacenter, with severe societal and business losses. For this reason, automated approaches to detect thermal anomalies in datacenters have considerable potential. This thesis analyzed and characterized the power and thermal characteristics of a Tier0 datacenter (CINECA) during production and under abnormal thermal conditions. Then, a Deep Learning (DL)-powered thermal hazard prediction framework is proposed. The proposed models are validated against real thermal hazard events reported for the studied HPC cluster while in production. This thesis is the first empirical study of thermal anomaly detection and prediction techniques of a real large-scale HPC system to the best of my knowledge. For this thesis, I used a large-scale dataset, monitoring data of tens of thousands of sensors for around 24 months with a data collection rate of around 20 seconds.

Damage Identification of Structures through Vibration-Based Structural Monitoring Systems

The thesis has been carried out within the “SHAPE Project - Predicting Strength Changes in Bridges from Frequency Data Safety, Hazard, and Poly-harmonic Evaluation” (ERA-NET Plus Infravation Call 2014) which dealt with the structural assessment of existing bridges and laboratory structural reproductions through the use of vibration-based monitoring systems, for detecting changes in their natural frequencies and correlating them with the occurrence of damage. The main purpose of this PhD dissertation has been the detection of the variation of the main natural frequencies as a consequence of a previous-established damage configuration provided on a structure. Firstly, the effect of local damage on the modal feature has been discussed mainly concerning a steel frame and a composite steel-concrete bridge. Concerning the variation of the fundamental frequency of the small bridge, the increasing severity of two local damages has been investigated. Moreover, the comparison with a 3D FE model is even presented establishing a link between the dynamic properties and the damage features. Then, moving towards a diffused damage pattern, four concrete beams and a small concrete deck were loaded achieving the yielding of the steel reinforcement. The stiffness deterioration in terms of frequency shifts has been reconsidered by collecting a large set of dynamic experiments on simply supported R.C. beams discussed in the literature. The comparison of the load-frequency curves suggested a significant agreement among all the experiments. Thus, in the framework of damage mechanics, the “breathing cracks” phenomenon has been discussed leading to an analytical formula able to explain the frequency decay observed experimentally. Lastly, some dynamic investigations of two existing bridges and the corresponding FE Models are presented in Chapter 4. Moreover, concerning the bridge in Bologna, two prototypes of a network of accelerometers were installed and the data of a few months of monitoring have been discussed.

Data sensitivity detection in chat interactions for privacy protection

In recent years, there has been exponential growth in using virtual spaces, including dialogue systems, that handle personal information. The concept of personal privacy in the literature is discussed and controversial, whereas, in the technological field, it directly influences the degree of reliability perceived in the information system (privacy ‘as trust’). This work aims to protect the right to privacy on personal data (GDPR, 2018) and avoid the loss of sensitive content by exploring sensitive information detection (SID) task. It is grounded on the following research questions: (RQ1) What does sensitive data mean? How to define a personal sensitive information domain? (RQ2) How to create a state-of-the-art model for SID?(RQ3) How to evaluate the model? RQ1 theoretically investigates the concepts of privacy and the ontological state-of-the-art representation of personal information. The Data Privacy Vocabulary (DPV) is the taxonomic resource taken as an authoritative reference for the definition of the knowledge domain. Concerning RQ2, we investigate two approaches to classify sensitive data: the first - bottom-up - explores automatic learning methods based on transformer networks, the second - top-down - proposes logical-symbolic methods with the construction of privaframe, a knowledge graph of compositional frames representing personal data categories. Both approaches are tested. For the evaluation - RQ3 – we create SPeDaC, a sentence-level labeled resource. This can be used as a benchmark or training in the SID task, filling the gap of a shared resource in this field. If the approach based on artificial neural networks confirms the validity of the direction adopted in the most recent studies on SID, the logical-symbolic approach emerges as the preferred way for the classification of fine-grained personal data categories, thanks to the semantic-grounded tailor modeling it allows. At the same time, the results highlight the strong potential of hybrid architectures in solving automatic tasks.

Radio frequency communication and fault detection for railway signalling

The continuous and swift progression of both wireless and wired communication technologies in today's world owes its success to the foundational systems established earlier. These systems serve as the building blocks that enable the enhancement of services to cater to evolving requirements. Studying the vulnerabilities of previously designed systems and their current usage leads to the development of new communication technologies replacing the old ones such as GSM-R in the railway field. The current industrial research has a specific focus on finding an appropriate telecommunication solution for railway communications that will replace the GSM-R standard which will be switched off in the next years. Various standardization organizations are currently exploring and designing a radiofrequency technology based standard solution to serve railway communications in the form of FRMCS (Future Railway Mobile Communication System) to substitute the current GSM-R. Bearing on this topic, the primary strategic objective of the research is to assess the feasibility to leverage on the current public network technologies such as LTE to cater to mission and safety critical communication for low density lines. The research aims to identify the constraints, define a service level agreement with telecom operators, and establish the necessary implementations to make the system as reliable as possible over an open and public network, while considering safety and cybersecurity aspects. The LTE infrastructure would be utilized to transmit the vital data for the communication of a railway system and to gather and transmit all the field measurements to the control room for maintenance purposes. Given the significance of maintenance activities in the railway sector, the ongoing research includes the implementation of a machine learning algorithm to detect railway equipment faults, reducing time and human analysis errors due to the large volume of measurements from the field.

Directly training spiking neural networks for cyber-physical systems: from supervised to reinforcement learning

Spiking Neural Networks (SNNs) are bio-inspired Artificial Neural Networks (ANNs) utilizing discrete spiking signals, akin to neuron communication in the brain, making them ideal for real-time and energy-efficient Cyber-Physical Systems (CPSs). This thesis explores their potential in Structural Health Monitoring (SHM), leveraging low-cost MEMS accelerometers for early damage detection in motorway bridges. The study focuses on Long Short-Term SNNs (LSNNs), although their complex learning processes pose challenges. Comparing LSNNs with other ANN models and training algorithms for SHM, findings indicate LSNNs' effectiveness in damage identification, comparable to ANNs trained using traditional methods. Additionally, an optimized embedded LSNN implementation demonstrates a 54% reduction in execution time, but with longer pre-processing due to spike-based encoding. Furthermore, SNNs are applied in UAV obstacle avoidance, trained directly using a Reinforcement Learning (RL) algorithm with event-based input from a Dynamic Vision Sensor (DVS). Performance evaluation against Convolutional Neural Networks (CNNs) highlights SNNs' superior energy efficiency, showing a 6x decrease in energy consumption. The study also investigates embedded SNN implementations' latency and throughput in real-world deployments, emphasizing their potential for energy-efficient monitoring systems. This research contributes to advancing SHM and UAV obstacle avoidance through SNNs' efficient information processing and decision-making capabilities within CPS domains.

Failure detection for transport processes on networks

Diffusion on networks is a convenient framework to describe transport systems of different nature (from biological transport systems to urban mobility). The mathematical models are based on master equations that describe the diffusion processes by means of the weighted Laplacian matrix that connects the nodes. The link weight represent the coupling strength between the nodes. In this thesis we cope with the problem of localizing a single-edge failure that occurs in the network. An edge failure is meant to be as a sudden decrease of its transport capacities. An incomplete observation of the dynamical state of the network is available. An optimal clustering procedure based on the correlation properties among the node states is proposed. The network dimensionality is then reduced introducing representative nodes for each cluster, whose dynamical state is observed. We check the efficiency of the failure localization for our clustering method in comparison with more traditional techniques, using different graph configurations.

A novel approach to Non-Orthogonal Random Access with application to NB-IoT NTN systems

In the Massive IoT vision, millions of devices need to be connected to the Internet through a wireless access technology. However, current IoT-focused standards are not fully prepared for this future. In this thesis, a novel approach to Non-Orthogonal techniques for Random Access, which is the main bottleneck in high density systems, is proposed. First, the most popular wireless access standards are presented, with a focus on Narrowband-IoT. Then, the Random Access procedure as implemented in NB-IoT is analyzed. The Non-Orthogonal Random Access technique is presented next, along with two potential algorithms for the detection of non-orthogonal preambles. Finally, the performance of the proposed solutions are obtained through numerical simulations.

Industrial Demilitarized Zone and Zero Trust cybersecurity models for Industrial Control Systems

Today more than ever, with the recent war in Ukraine and the increasing number of attacks that affect systems of nations and companies every day, the world realizes that cybersecurity can no longer be considered just as a “cost”. It must become a pillar for our infrastructures that involve the security of our nations and the safety of people. Critical infrastructure, like energy, financial services, and healthcare, have become targets of many cyberattacks from several criminal groups, with an increasing number of resources and competencies, putting at risk the security and safety of companies and entire nations. This thesis aims to investigate the state-of-the-art regarding the best practice for securing Industrial control systems. We study the differences between two security frameworks. The first is Industrial Demilitarized Zone (I-DMZ), a perimeter-based security solution. The second one is the Zero Trust Architecture (ZTA) which removes the concept of perimeter to offer an entirely new approach to cybersecurity based on the slogan ‘Never Trust, always verify’. Starting from this premise, the Zero Trust model embeds strict Authentication, Authorization, and monitoring controls for any access to any resource. We have defined two architectures according to the State-of-the-art and the cybersecurity experts’ guidelines to compare I-DMZ, and Zero Trust approaches to ICS security. The goal is to demonstrate how a Zero Trust approach dramatically reduces the possibility of an attacker penetrating the network or moving laterally to compromise the entire infrastructure. A third architecture has been defined based on Cloud and fog/edge computing technology. It shows how Cloud solutions can improve the security and reliability of infrastructure and production processes that can benefit from a range of new functionalities, that the Cloud could offer as-a-Service.We have implemented and tested our Zero Trust solution and its ability to block intrusion or attempted attacks.

A Microleakage Study Of Gutta-percha/ah Plus And Resilon/real Self-etch Systems After Different Irrigation Protocols.

The development and maintenance of the sealing of the root canal system is the key to the success of root canal treatment. The resin-based adhesive material has the potential to reduce the microleakage of the root canal because of its adhesive properties and penetration into dentinal walls. Moreover, the irrigation protocols may have an influence on the adhesiveness of resin-based sealers to root dentin. The objective of the present study was to evaluate the effect of different irrigant protocols on coronal bacterial microleakage of gutta-percha/AH Plus and Resilon/Real Seal Self-etch systems. One hundred ninety pre-molars were used. The teeth were divided into 18 experimental groups according to the irrigation protocols and filling materials used. The protocols used were: distilled water; sodium hypochlorite (NaOCl)+eDTA; NaOCl+H3PO4; NaOCl+eDTA+chlorhexidine (CHX); NaOCl+H3PO4+CHX; CHX+eDTA; CHX+ H3PO4; CHX+eDTA+CHX and CHX+H3PO4+CHX. Gutta-percha/AH Plus or Resilon/Real Seal Se were used as root-filling materials. The coronal microleakage was evaluated for 90 days against Enterococcus faecalis. Data were statistically analyzed using Kaplan-Meier survival test, Kruskal-Wallis and Mann-Whitney tests. No significant difference was verified in the groups using chlorhexidine or sodium hypochlorite during the chemo-mechanical preparation followed by eDTA or phosphoric acid for smear layer removal. The same results were found for filling materials. However, the statistical analyses revealed that a final flush with 2% chlorhexidine reduced significantly the coronal microleakage. A final flush with 2% chlorhexidine after smear layer removal reduces coronal microleakage of teeth filled with gutta-percha/AH Plus or Resilon/Real Seal SE.