Intelligent Sensor Systems for Structural Health Monitoring Applications

The convergence between the recent developments in sensing technologies, data science, signal processing and advanced modelling has fostered a new paradigm to the Structural Health Monitoring (SHM) of engineered structures, which is the one based on intelligent sensors, i.e., embedded devices capable of stream processing data and/or performing structural inference in a self-contained and near-sensor manner. To efficiently exploit these intelligent sensor units for full-scale structural assessment, a joint effort is required to deal with instrumental aspects related to signal acquisition, conditioning and digitalization, and those pertaining to data management, data analytics and information sharing. In this framework, the main goal of this Thesis is to tackle the multi-faceted nature of the monitoring process, via a full-scale optimization of the hardware and software resources involved by the {SHM} system. The pursuit of this objective has required the investigation of both: i) transversal aspects common to multiple application domains at different abstraction levels (such as knowledge distillation, networking solutions, microsystem {HW} architectures), and ii) the specificities of the monitoring methodologies (vibrations, guided waves, acoustic emission monitoring). The key tools adopted in the proposed monitoring frameworks belong to the embedded signal processing field: namely, graph signal processing, compressed sensing, ARMA System Identification, digital data communication and TinyML.

Development and Implementation of the Plasma Focus Technology for Radiation Therapy Applications

A Plasma Focus device can confine in a small region a plasma generated during the pinch phase. When the plasma is in the pinch condition it creates an environment that produces several kinds of radiations. When the filling gas is nitrogen, a self-collimated backwardly emitted electron beam, slightly spread by the coulomb repulsion, can be considered one of the most interesting outputs. That beam can be converted into X-ray pulses able to transfer energy at an Ultra-High Dose-Rate (UH-DR), up to 1 Gy pulse-1, for clinical applications, research, or industrial purposes. The radiation fields have been studied with the PFMA-3 hosted at the University of Bologna, finding the radiation behavior at different operating conditions and working parameters for a proper tuning of this class of devices in clinical applications. The experimental outcomes have been compared with available analytical formalisms as benchmark and the scaling laws have been proposed. A set of Monte Carlo models have been built with direct and adjoint techniques for an accurate X-ray source characterization and for setting fast and reliable irradiation planning for patients. By coupling deterministic and Monte Carlo codes, a focusing lens for the charged particles has been designed for obtaining a beam suitable for applications as external radiotherapy or intra-operative radiation therapy. The radiobiological effectiveness of the UH PF DR, a FLASH source, has been evaluated by coupling different Monte Carlo codes estimating the overall level of DNA damage at the multi-cellular and tissue levels by considering the spatial variation effects as well as the radiation field characteristics. The numerical results have been correlated to the experimental outcomes. Finally, ambient dose measurements have been performed for tuning the numerical models and obtaining doses for radiation protection purposes. The PFMA-3 technology has been fully characterized toward clinical implementation and installation in a medical facility.

Applications of network-based approaches in drug research

Recent research trends in computer-aided drug design have shown an increasing interest towards the implementation of advanced approaches able to deal with large amount of data. This demand arose from the awareness of the complexity of biological systems and from the availability of data provided by high-throughput technologies. As a consequence, drug research has embraced this paradigm shift exploiting approaches such as that based on networks. Indeed, the process of drug discovery can benefit from the implementation of network-based methods at different steps from target identification to drug repurposing. From this broad range of opportunities, this thesis is focused on three main topics: (i) chemical space networks (CSNs), which are designed to represent and characterize bioactive compound data sets; (ii) drug-target interactions (DTIs) prediction through a network-based algorithm that predicts missing links; (iii) COVID-19 drug research which was explored implementing COVIDrugNet, a network-based tool for COVID-19 related drugs. The main highlight emerged from this thesis is that network-based approaches can be considered useful methodologies to tackle different issues in drug research. In detail, CSNs are valuable coordinate-free, graphically accessible representations of structure-activity relationships of bioactive compounds data sets especially for medium-large libraries of molecules. DTIs prediction through the random walk with restart algorithm on heterogeneous networks can be a helpful method for target identification. COVIDrugNet is an example of the usefulness of network-based approaches for studying drugs related to a specific condition, i.e., COVID-19, and the same ‘systems-based’ approaches can be used for other diseases. To conclude, network-based tools are proving to be suitable in many applications in drug research and provide the opportunity to model and analyze diverse drug-related data sets, even large ones, also integrating different multi-domain information.

High performance and energy-efficient instruction cache design and optimisation for ultra-low-power multi-core clusters

High Energy efficiency and high performance are the key regiments for Internet of Things (IoT) end-nodes. Exploiting cluster of multiple programmable processors has recently emerged as a suitable solution to address this challenge. However, one of the main bottlenecks for multi-core architectures is the instruction cache. While private caches fall into data replication and wasting area, fully shared caches lack scalability and form a bottleneck for the operating frequency. Hence we propose a hybrid solution where a larger shared cache (L1.5) is shared by multiple cores connected through a low-latency interconnect to small private caches (L1). However, it is still limited by large capacity miss with a small L1. Thus, we propose a sequential prefetch from L1 to L1.5 to improve the performance with little area overhead. Moreover, to cut the critical path for better timing, we optimized the core instruction fetch stage with non-blocking transfer by adopting a 4 x 32-bit ring buffer FIFO and adding a pipeline for the conditional branch. We present a detailed comparison of different instruction cache architectures' performance and energy efficiency recently proposed for Parallel Ultra-Low-Power clusters. On average, when executing a set of real-life IoT applications, our two-level cache improves the performance by up to 20% and loses 7% energy efficiency with respect to the private cache. Compared to a shared cache system, it improves performance by up to 17% and keeps the same energy efficiency. In the end, up to 20% timing (maximum frequency) improvement and software control enable the two-level instruction cache with prefetch adapt to various battery-powered usage cases to balance high performance and energy efficiency.

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.

Analysis, Design, and Hardware-in-the-Loop Validation of Multi-Active Bridge Converters

This master's thesis investigates different aspects of Dual-Active-Bridge (DAB) Converter and extends aspects further to Multi-Active-Bridges (MAB). The thesis starts with an overview of the applications of the DAB and MAB and their importance. The analytical part of the thesis includes the derivation of the peak and RMS currents, which is required for finding the losses present in the system. The power converters, considered in this thesis are DAB, Triple-Active Bridge (TAB) and Quad-Active Bridge (QAB). All the theoretical calculations are compared with the simulation results from PLECS software for identifying the correctness of the reviewed and developed theory. The Hardware-in-the-Loop (HIL) simulation is conducted for checking the control operation in real-time with the help of the RT box from the Plexim. Additionally, as in real systems digital signal processor (DSP), system-on-chip or field programmable gate array is employed for the control of the power electronic systems, and the execution of the control in the real-time simulation (RTS) conducted is performed by DSP.

An Efficient Protocol For The Generation Of Monocyte Derived Dendritic Cells Using Serum-free Media For Clinical Applications In Post Remission Aml Patients.

Protocols for the generation of dendritic cells (DCs) using serum as a supplementation of culture media leads to reactions due to animal proteins and disease transmissions. Several types of serum-free media (SFM), based on good manufacture practices (GMP), have recently been used and seem to be a viable option. The aim of this study was to evaluate the results of the differentiation, maturation, and function of DCs from Acute Myeloid Leukemia patients (AML), generated in SFM and medium supplemented with autologous serum (AS). DCs were analyzed by phenotype characteristics, viability, and functionality. The results showed the possibility of generating viable DCs in all the conditions tested. In patients, the X-VIVO 15 medium was more efficient than the other media tested in the generation of DCs producing IL-12p70 (p=0.05). Moreover, the presence of AS led to a significant increase of IL-10 by DCs as compared with CellGro (p=0.05) and X-Vivo15 (p=0.05) media, both in patients and donors. We concluded that SFM was efficient in the production of DCs for immunotherapy in AML patients. However, the use of AS appears to interfere with the functional capacity of the generated DCs.

Graphene And Carbon Nanotube Nanocomposite For Gene Transfection.

Graphene and carbon nanotube nanocomposite (GCN) was synthesised and applied in gene transfection of pIRES plasmid conjugated with green fluorescent protein (GFP) in NIH-3T3 and NG97 cell lines. The tips of the multi-walled carbon nanotubes (MWCNTs) were exfoliated by oxygen plasma etching, which is also known to attach oxygen content groups on the MWCNT surfaces, changing their hydrophobicity. The nanocomposite was characterised by high resolution scanning electron microscopy; energy-dispersive X-ray, Fourier transform infrared and Raman spectroscopies, as well as zeta potential and particle size analyses using dynamic light scattering. BET adsorption isotherms showed the GCN to have an effective surface area of 38.5m(2)/g. The GCN and pIRES plasmid conjugated with the GFP gene, forming π-stacking when dispersed in water by magnetic stirring, resulting in a helical wrap. The measured zeta potential confirmed that the plasmid was connected to the nanocomposite. The NIH-3T3 and NG97 cell lines could phagocytize this wrap. The gene transfection was characterised by fluorescent protein produced in the cells and pictured by fluorescent microscopy. Before application, we studied GCN cell viability in NIH-3T3 and NG97 line cells using both MTT and Neutral Red uptake assays. Our results suggest that GCN has moderate stability behaviour as colloid solution and has great potential as a gene carrier agent in non-viral based therapy, with low cytotoxicity and good transfection efficiency.

Doehlert Design-desirability Function Multi-criteria Optimal Separation Of 17 Phenolic Compounds From Extra-virgin Olive Oil By Capillary Zone Electrophoresis.

In Brazil, the consumption of extra-virgin olive oil (EVOO) is increasing annually, but there are no experimental studies concerning the phenolic compound contents of commercial EVOO. The aim of this work was to optimise the separation of 17 phenolic compounds already detected in EVOO. A Doehlert matrix experimental design was used, evaluating the effects of pH and electrolyte concentration. Resolution, runtime and migration time relative standard deviation values were evaluated. Derringer's desirability function was used to simultaneously optimise all 37 responses. The 17 peaks were separated in 19min using a fused-silica capillary (50μm internal diameter, 72cm of effective length) with an extended light path and 101.3mmolL(-1) of boric acid electrolyte (pH 9.15, 30kV). The method was validated and applied to 15 EVOO samples found in Brazilian supermarkets.

Applications Of Visual Evoked Potentials And Fourier-domain Optical Coherence Tomography In Parkinson's Disease: A Controlled Study.

The goal of this cross-sectional observational study was to quantify the pattern-shift visual evoked potentials (VEP) and the thickness as well as the volume of retinal layers using optical coherence tomography (OCT) across a cohort of Parkinson's disease (PD) patients and age-matched controls. Forty-three PD patients and 38 controls were enrolled. All participants underwent a detailed neurological and ophthalmologic evaluation. Idiopathic PD cases were included. Cases with glaucoma or increased intra-ocular pressure were excluded. Patients were assessed by VEP and high-resolution Fourier-domain OCT, which quantified the inner and outer thicknesses of the retinal layers. VEP latencies and the thicknesses of the retinal layers were the main outcome measures. The mean age, with standard deviation (SD), of the PD patients and controls were 63.1 (7.5) and 62.4 (7.2) years, respectively. The patients were predominantly in the initial Hoehn-Yahr (HY) disease stages (34.8% in stage 1 or 1.5, and 55.8 % in stage 2). The VEP latencies and the thicknesses as well as the volumes of the retinal inner and outer layers of the groups were similar. A negative correlation between the retinal thickness and the age was noted in both groups. The thickness of the retinal nerve fibre layer (RNFL) was 102.7 μm in PD patients vs. 104.2 μm in controls. The thicknesses of retinal layers, VEP, and RNFL of PD patients were similar to those of the controls. Despite the use of a representative cohort of PD patients and high-resolution OCT in this study, further studies are required to establish the validity of using OCT and VEP measurements as the anatomic and functional biomarkers for the evaluation of retinal and visual pathways in PD patients.

Microfluidic Paper-based Devices For Bioanalytical Applications.

Paper has become increasingly recognized as a very interesting substrate for the construction of microfluidic devices, with potential application in a variety of areas, including health diagnosis, environmental monitoring, immunoassays and food safety. The aim of this review is to present a short history of analytical systems constructed from paper, summarize the main advantages and disadvantages of fabrication techniques, exploit alternative methods of detection such as colorimetric, electrochemical, photoelectrochemical, chemiluminescence and electrochemiluminescence, as well as to take a closer look at the novel achievements in the field of bioanalysis published during the last 2 years. Finally, the future trends for production of such devices are discussed.

Behavior Of Listeria Monocytogenes In A Multi-species Biofilm With Enterococcus Faecalis And Enterococcus Faecium And Control Through Sanitation Procedures.

The formation of mono-species biofilm (Listeria monocytogenes) and multi-species biofilms (Enterococcus faecium, Enterococcus faecalis, and L. monocytogenes) was evaluated. In addition, the effectiveness of sanitation procedures for the control of the multi-species biofilm also was evaluated. The biofilms were grown on stainless steel coupons at various incubation temperatures (7, 25 and 39°C) and contact times (0, 1, 2, 4, 6 and 8days). In all tests, at 7°C, the microbial counts were below 0.4 log CFU/cm(2) and not characteristic of biofilms. In mono-species biofilm, the counts of L. monocytogenes after 8days of contact were 4.1 and 2.8 log CFU/cm(2) at 25 and 39°C, respectively. In the multi-species biofilms, Enterococcus spp. were present at counts of 8 log CFU/cm(2) at 25 and 39°C after 8days of contact. However, the L. monocytogenes in multi-species biofilms was significantly affected by the presence of Enterococcus spp. and by temperature. At 25°C, the growth of L. monocytogenes biofilms was favored in multi-species cultures, with counts above 6 log CFU/cm(2) after 8days of contact. In contrast, at 39°C, a negative effect was observed for L. monocytogenes biofilm growth in mixed cultures, with a significant reduction in counts over time and values below 0.4 log CFU/cm(2) starting at day 4. Anionic tensioactive cleaning complemented with another procedure (acid cleaning, disinfection or acid cleaning+disinfection) eliminated the multi-species biofilms under all conditions tested (counts of all micro-organisms<0.4 log CFU/cm(2)). Peracetic acid was the most effective disinfectant, eliminating the multi-species biofilms under all tested conditions (counts of the all microorganisms <0.4 log CFU/cm(2)). In contrast, biguanide was the least effective disinfectant, failing to eliminate biofilms under all the test conditions.

PARAFAC: uma ferramenta quimiométrica para tratamento de dados multidimensionais. Aplicações na determinação direta de fármacos em plasma humano por espectrofluorimetria

Since the last decade, the combined use of chemometrics and molecular spectroscopic techniques has become a new alternative for direct drug determination, without the need of physical separation. Among the new methodologies developed, the application of PARAFAC in the decomposition of spectrofluorimetric data should be highlighted. The first objective of this article is to describe the theoretical basis of PARAFAC. For this purpose, a discussion about the order of chemometric methods used in multivariate calibration and the development of multi-dimensional methods is presented first. The other objective of this article is to divulge for the Brazilian chemical community the potential of the combination PARAFAC/spectrofluorimetry for the determination of drugs in complex biological matrices. For this purpose, two applications aiming at determining, respectively, doxorrubicine and salicylate in human plasma are presented.

Critical applications of SW 846 US EPA methods to evaluation of marine samples quality

Technical evaluation of analytical data is of extreme relevance considering it can be used for comparisons with environmental quality standards and decision-making as related to the management of disposal of dredged sediments and the evaluation of salt and brackish water quality in accordance with CONAMA 357/05 Resolution. It is, therefore, essential that the project manager discusses the environmental agency's technical requirements with the laboratory contracted for the follow-up of the analysis underway and even with a view to possible re-analysis when anomalous data are identified. The main technical requirements are: (1) method quantitation limits (QLs) should fall below environmental standards; (2) analyses should be carried out in laboratories whose analytical scope is accredited by the National Institute of Metrology (INMETRO) or qualified or accepted by a licensing agency; (3) chain of custody should be provided in order to ensure sample traceability; (4) control charts should be provided to prove method performance; (5) certified reference material analysis or, if that is not available, matrix spike analysis, should be undertaken and (6) chromatograms should be included in the analytical report. Within this context and with a view to helping environmental managers in analytical report evaluation, this work has as objectives the discussion of the limitations of the application of SW 846 US EPA methods to marine samples, the consequences of having data based on method detection limits (MDL) and not sample quantitation limits (SQL), and present possible modifications of the principal method applied by laboratories in order to comply with environmental quality standards.

A multi-screening approach for marine-derived fungal metabolites and the isolation of cyclodepsipeptides from Beauveria felina

Extracts obtained from 57 marine-derived fungal strains were analyzed by HPLC-PDA, TLC and ¹H NMR. The analyses showed that the growth conditions affected the chemical profile of crude extracts. Furthermore, the majority of fungal strains which produced either bioactive of chemically distinctive crude extracts have been isolated from sediments or marine algae. The chemical investigation of the antimycobacterial and cytotoxic crude extract obtained from two strains of the fungus Beauveria felina have yielded cyclodepsipeptides related to destruxins. The present approach constitutes a valuable tool for the selection of fungal strains that produce chemically interesting or biologically active secondary metabolites.