Modelling and predictive control techniques for building heating systems

Model predictive control (MPC) has often been referred to in literature as a potential method for more efficient control of building heating systems. Though a significant performance improvement can be achieved with an MPC strategy, the complexity introduced to the commissioning of the system is often prohibitive. Models are required which can capture the thermodynamic properties of the building with sufficient accuracy for meaningful predictions to be made. Furthermore, a large number of tuning weights may need to be determined to achieve a desired performance. For MPC to become a practicable alternative, these issues must be addressed. Acknowledging the impact of the external environment as well as the interaction of occupants on the thermal behaviour of the building, in this work, techniques have been developed for deriving building models from data in which large, unmeasured disturbances are present. A spatio-temporal filtering process was introduced to determine estimates of the disturbances from measured data, which were then incorporated with metaheuristic search techniques to derive high-order simulation models, capable of replicating the thermal dynamics of a building. While a high-order simulation model allowed for control strategies to be analysed and compared, low-order models were required for use within the MPC strategy itself. The disturbance estimation techniques were adapted for use with system-identification methods to derive such models. MPC formulations were then derived to enable a more straightforward commissioning process and implemented in a validated simulation platform. A prioritised-objective strategy was developed which allowed for the tuning parameters typically associated with an MPC cost function to be omitted from the formulation by separation of the conflicting requirements of comfort satisfaction and energy reduction within a lexicographic framework. The improved ability of the formulation to be set-up and reconfigured in faulted conditions was shown.

Production Diseases in European Organic Dairy Farms – Current Status and Identification of Drivers for Improvement by Means of a Systemic Approach

The aims of this thesis were to determine the animal health status in organic dairy farms in Europe and to identify drivers for improving the current situation by means of a systemic approach. Prevalences of production diseases were determined in 192 herds in Germany, France, Spain, and Sweden (Paper I), and stakeholder consultations were performed to investigate potential drivers to improve animal health on the sector level (ibid.). Interactions between farm variables were assessed through impact analysis and evaluated to identify general system behaviour and classify components according to their outgoing and incoming impacts (Paper II-III). The mean values and variances of prevalences indicate that the common rules of organic dairy farming in Europe do not result in consistently low levels of production diseases. Stakeholders deemed it necessary to improve the current status and were generally in favour of establishing thresholds for the prevalence of production diseases in organic dairy herds as well as taking actions to improve farms below that threshold. In order to close the gap between the organic principle of health and the organic farming practice, there is the need to formulate a common objective of good animal health and to install instruments to ensure and prove that the aim is followed by all dairy farmers in Europe who sell their products under the organic label. Regular monitoring and evaluation of herd health performance based on reference values are considered preconditions for identifying farms not reaching the target and thus in need of improvement. Graph-based impact analysis was shown to be a suitable method for modeling and evaluating the manifold interactions between farm factors and for identifying the most influential components on the farm level taking into account direct and indirect impacts as well as impact strengths. Variables likely to affect the system as a whole, and the prevalence of production diseases in particular, varied largely between farms despite some general tendencies. This finding reflects the diversity of farm systems and underlines the importance of applying systemic approaches in health management. Reducing the complexity of farm systems and indicating farm-specific drivers, i.e. areas in a farm, where changes will have a large impact, the presented approach has the potential to complement and enrich current advisory practice and to support farmers’ decision-making in terms of animal health.

Identification of novel molecular determinants of tissue mineralization in fish

Tese de doutoramento, Ciências Biomédicas, Departamento de Ciências Biomédicas e Medicina, Universidade do Algarve, 2015

Using modeling tools for implementing feasible land use and nature conservation governance systems in small islands e The Pico Island (Azores) case-study

The present study deals with the development of systematic conservation planning as management instrument in small oceanic islands, ensuring open systems of governance, and able to integrate an informed and involved participation of the stakeholders. Marxan software was used to define management areas according a set of alternative land use scenarios considering different conservation and management paradigms. Modeled conservation zones were interpreted and compared with the existing protected areas allowing more fused information for future trade-outs and stakeholder's involvement. The results, allowing the identification of Target Management Units (TMU) based on the consideration of different development scenarios proved to be consistent with a feasible development of evaluation approaches able to support sound governance systems. Moreover, the detailed geographic identification of TMU seems to be able to support participated policies towards a more sustainable management of the entire island

Large scale underground energy storage for renewables integration: general criteria for reservoir identification and viable technologies.

The increasing integration of renewable energies in the electricity grid contributes considerably to achieve the European Union goals on energy and Greenhouse Gases (GHG) emissions reduction. However, it also brings problems to grid management. Large scale energy storage can provide the means for a better integration of the renewable energy sources, for balancing supply and demand, to increase energy security, to enhance a better management of the grid and also to converge towards a low carbon economy. Geological formations have the potential to store large volumes of fluids with minimal impact to environment and society. One of the ways to ensure a large scale energy storage is to use the storage capacity in geological reservoir. In fact, there are several viable technologies for underground energy storage, as well as several types of underground reservoirs that can be considered. The geological energy storage technologies considered in this research were: Underground Gas Storage (UGS), Hydrogen Storage (HS), Compressed Air Energy Storage (CAES), Underground Pumped Hydro Storage (UPHS) and Thermal Energy Storage (TES). For these different types of underground energy storage technologies there are several types of geological reservoirs that can be suitable, namely: depleted hydrocarbon reservoirs, aquifers, salt formations and caverns, engineered rock caverns and abandoned mines. Specific site screening criteria are applicable to each of these reservoir types and technologies, which determines the viability of the reservoir itself, and of the technology for any particular site. This paper presents a review of the criteria applied in the scope of the Portuguese contribution to the EU funded project ESTMAP – Energy Storage Mapping and Planning.

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 of a Whole-Genome Sequencing (WGS) approach for the characterization of Legionella environmental isolates in water systems

Legionella is a Gram-negative bacterium that represent a public health issue, with heavy social and economic impact. Therefore, it is mandatory to provide a proper environmental surveillance and risk assessment plan to perform Legionella control in water distribution systems in hospital and community buildings. The thesis joins several methodologies in a unique workflow applied for the identification of non-pneumophila Legionella species (n-pL), starting from standard methods as culture and gene sequencing (mip and rpoB), and passing through innovative approaches as MALDI-TOF MS technique and whole genome sequencing (WGS). The results obtained, were compared to identify the Legionella isolates, and lead to four presumptive novel Legionella species identification. One of these four new isolates was characterized and recognized at taxonomy level with the name of Legionella bononiensis (the 64th Legionella species). The workflow applied in this thesis, help to increase the knowledge of Legionella environmental species, improving the description of the environment itself and the events that promote the growth of Legionella in their ecological niche. The correct identification and characterization of the isolates permit to prevent their spread in man-made environment and contain the occurrence of cases, clusters, or outbreaks. Therefore, the experimental work undertaken, could support the preventive measures during environmental and clinical surveillance, improving the study of species often underestimated or still unknown.

Big data analysis systems in IoE environments for managing privacy and data protection: pseudonymity, de-anonymization and the right to be forgotten

The thesis represents the conclusive outcome of the European Joint Doctorate programmein Law, Science & Technology funded by the European Commission with the instrument Marie Skłodowska-Curie Innovative Training Networks actions inside of the H2020, grantagreement n. 814177. The tension between data protection and privacy from one side, and the need of granting further uses of processed personal datails is investigated, drawing the lines of the technological development of the de-anonymization/re-identification risk with an explorative survey. After acknowledging its span, it is questioned whether a certain degree of anonymity can still be granted focusing on a double perspective: an objective and a subjective perspective. The objective perspective focuses on the data processing models per se, while the subjective perspective investigates whether the distribution of roles and responsibilities among stakeholders can ensure data anonymity.

Early-stage deformation localisation in thrust systems: new perspectives from carbonates in the Italian Southern Alps and Oman Mountains

This thesis investigates mechanisms and boundary conditions that steer the early localisation of deformation and strain in carbonate multilayers involved in thrust systems, under shallow and mid-crustal conditions. Much is already understood about deformation localisation, but some key points remain loosely constrained. They encompass i) the understanding of which structural domains can preserve evidence of early stages of tectonic shortening, ii) the recognition of which mechanisms assist deformation during these stages and iii) the identification of parameters that actually steer the beginning of localisation. To clarify these points, the thesis presents the results of an integrated, multiscale and multi-technique structural study that relied on field and laboratory data to analyse the structural, architectural, mineralogical and geochemical features that govern deformation during compressional tectonics. By focusing on two case studies, the Eastern Southern Alps (northern Italy), where deformation is mainly brittle, and the Oman Mountains (northeastern Oman), where ductile deformation dominates, the thesis shows that the deformation localisation is steered by several mechanisms that mutually interact at different stages during compression. At shallow crustal conditions, derived conceptual and numerical models show that both inherited (e.g., stratigraphic) and acquired (e.g., structural) features play a key role in steering deformation and differentiating the seismic behaviour of the multilayer succession. At the same time, at deeper crustal conditions, strain localises in narrow domains in which fluids, temperature, shear strain and pressure act together during the development of the internal fabric and the chemical composition of mylonitic shear zones, in which localisation took place under high-pressure (HP) and low-temperature (LT) conditions. In particular, results indicate that those shear zones acted as “sheltering structural capsules” in which peculiar processes can happen and where the results of these processes can be successively preserved even over hundreds of millions of years.

Identification of sunflower genotypes suitable for organic agriculture and development of analytical tools for their traceability

Thanks to the development and combination of molecular markers for the genetic traceability of sunflower varieties and a gas chromatographic method for the determination of the FAs composition of sunflower oil, it was possible to implement an experimental method for the verification of both the traceability and the variety of organic sunflower marketed by Agricola Grains S.p.A. The experimental activity focused on two objectives: the implementation of molecular markers for the routine control of raw material deliveries for oil extraction and the improvement and validation of a gas chromatographic method for the determination of the FAs composition of sunflower oil. With regard to variety verification and traceability, the marker systems evaluated were the following: SSR markers (12) arranged in two multiplex sets and SCAR markers for the verification of cytoplasmic male sterility (Pet1) and fertility. In addition, two objectives were pursued in order to enable a routine application in the industrial field: the development of a suitable protocol for DNA extraction from single seeds and the implementation of a semi-automatic capillary electrophoresis system for the analysis of marker fragments. The development and validation of a new GC/FID analytical method for the determination of fatty acids (FAME) in sunflower achenes to improve the quality and efficiency of the analytical flow in the control of raw and refined materials entering the Agricola Grains S.p.A. production chain. The analytical performances being validated by the newly implemented method are: linearity of response, limit of quantification, specificity, precision, intra-laboratory precision, robustness, BIAS. These parameters are used to compare the newly developed method with the one considered as reference - Commission Regulation No. 2568/91 and Commission Implementing Regulation No. 2015/1833. Using the combination of the analytical methods mentioned above, the documentary traceability of the product can be confirmed experimentally, providing relevant information for subsequent marketing.

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.

Experimental evaluation of the z-resolution in different clinical Digital Breast Tomosynthesis systems using commercial phantoms

Digital Breast Tomosynthesis (DBT) is an advanced mammography technique based on the reconstruction of a pseudo-volumetric image. To date, image quality represents the most deficient section of DBT quality control protocols. In fact, related tests are not yet characterized by either action levels or typical values. This thesis work focuses on the evaluation of one aspect of image quality: the z-resolution. The latter is studied in terms of Artifact Spread Function (ASF), a function that describes the signal spread of a detail along the reconstructed focal planes. To quantify the ASF numerically, its Full Width at Half Maximum (FWHM) is calculated and used as a representative index of z-resolution. Experimental measurements were acquired in 24 DBT systems, of 7 different models, currently in use in 20 hospital facilities in Italy. The analysis, performed on the clinical reconstructed images, of 5 different commercial phantoms, lead to the identification of characteristic FWHM values for each type of DBT system. The ASF clearly showed a dependence on the size of the detail, providing higher FWHM values for larger objects. The z-resolution was found to be positively influenced by the acquisition angle: Fujifilm sistematically showed wider ASF profiles in ST mode (15°) than in HR mode (40°). However, no clear relationship was found between angular range and ASF, among different DBT systems, due to the influence of the peculiarities of each reconstruction algorithm. The experimental approach shown in this thesis work can be proposed as a z-resolution quality control test procedure. Contextually, the values found could be used as a starting point for identifying typical values to be included in the test, in a DBT protocol. Clearly, a statistically significant number of images is needed to do this. The equipment involved in this work is located in hospitals and is not available for research purposes, so only a limited amount of data was acquired and processed.

Mucopolysaccharidosis Type Ii: Identification Of 30 Novel Mutations Among Latin American Patients.

In this study, 103 unrelated South-American patients with mucopolysaccharidosis type II (MPS II) were investigated aiming at the identification of iduronate-2-sulfatase (IDS) disease causing mutations and the possibility of some insights on the genotype-phenotype correlation The strategy used for genotyping involved the identification of the previously reported inversion/disruption of the IDS gene by PCR and screening for other mutations by PCR/SSCP. The exons with altered mobility on SSCP were sequenced, as well as all the exons of patients with no SSCP alteration. By using this strategy, we were able to find the pathogenic mutation in all patients. Alterations such as inversion/disruption and partial/total deletions of the IDS gene were found in 20/103 (19%) patients. Small insertions/deletions/indels (<22 bp) and point mutations were identified in 83/103 (88%) patients, including 30 novel mutations; except for a higher frequency of small duplications in relation to small deletions, the frequencies of major and minor alterations found in our sample are in accordance with those described in the literature.

Identification Of Target Genes Using Gene Expression Profile Of Granulocytes From Patients With Chronic Myeloid Leukemia Treated With Tyrosine Kinase Inhibitors.

Differential gene expression analysis by suppression subtractive hybridization with correlation to the metabolic pathways involved in chronic myeloid leukemia (CML) may provide a new insight into the pathogenesis of CML. Among the overexpressed genes found in CML at diagnosis are SEPT5, RUNX1, MIER1, KPNA6 and FLT3, while PAN3, TOB1 and ITCH were decreased when compared to healthy volunteers. Some genes were identified and involved in CML for the first time, including TOB1, which showed a low expression in patients with CML during tyrosine kinase inhibitor treatment with no complete cytogenetic response. In agreement, reduced expression of TOB1 was also observed in resistant patients with CML compared to responsive patients. This might be related to the deregulation of apoptosis and the signaling pathway leading to resistance. Most of the identified genes were related to the regulation of nuclear factor κB (NF-κB), AKT, interferon and interleukin-4 (IL-4) in healthy cells. The results of this study combined with literature data show specific gene pathways that might be explored as markers to assess the evolution and prognosis of CML as well as identify new therapeutic targets.

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.