Pachymetry-Guided Intrastromal Air Injection ("Pachy-Bubble") for Deep Anterior Lamellar Keratoplasty

To evaluate an innovative technique for intrastromal air injection to achieve deep anterior lamellar keratoplasty (DALK) with bare Descemet membrane (DM). Thirty-four eyes with anterior corneal pathology, including 27 with keratoconus, underwent DALK. After 400 mm trephination with a suction trephine, ultrasound pachymetry was performed 0.8 mm internally from the trephination groove in the 11 to 1 o'clock position. In this area, a 2-mm incision was created, parallel to the groove, with a micrometer diamond knife calibrated to 90% depth of the thinnest measurement. A cannula was inserted through the incision and 0.5 mL of air was injected to dissect the DM from the stroma. After peripheral paracentesis, anterior keratectomy was carried out to bare the DM. A 0.25-mm oversized graft was sutured in place. Overall, 94.1% of eyes achieved DALK. Bare DM was achieved in 30 eyes, and a pre-DM dissection was performed in 2 eyes. Air injection was successful in detaching the DM (achieving the big bubble) in 88.2% of the eyes. In keratoconus eyes, the rate was 88.9%. All cases but one required a single air injection to achieve DM detachment. Microperforations occurred in 5 cases: 3 during manual layer-by-layer dissection after air injection failed to detach the DM, 1 during removal of the residual stroma after big-bubble formation, and 1 during the diamond knife incision. Two cases (5.9%) were converted to penetrating keratoplasty because of macroperforations. The technique was reproducible, safe, and highly effective in promoting DALK with bare DM.

Image Analysis Methods for Sugar Beet Phenotyping

In un mondo che richiede sempre maggiormente un'automazione delle attività della catena produttiva industriale, la computer vision rappresenta uno strumento fondamentale perciò che viene già riconosciuta internazionalmente come la Quarta Rivoluzione Industriale o Industry 4.0. Avvalendomi di questo strumento ho intrapreso presso l'azienda Syngenta lo studio della problematica della conta automatica del numero di foglie di una pianta. Il problema è stato affrontato utilizzando due differenti approcci, ispirandosi alla letteratura. All'interno dell'elaborato è presente anche la descrizione progettuale di un ulteriore metodo, ad oggi non presente in letteratura. Le metodologie saranno spiegate in dettaglio ed i risultati ottenuti saranno confrontati utilizzando i primi due approcci. Nel capitolo finale si trarranno le conclusioni sulle basi dei risultati ottenuti e dall'analisi degli stessi.

Graphical Representation of the Changes of Sector for Particular Cases in the Ponchon Savarit Method

A graphical and systematic analysis of particular cases where the compositions of the streams developed in the rectification column coincide with one of the vapor (yGFk) or liquid (xGFk) portions generated from the GFk can be found in this material (i.e.: yGFk=yk+1,1 or xGFk=xk,NTk).

Reducing task’s redundancy – the strategy of Faculty of Pharmacy Library to centralize all scientific research

Introduction – Based on a previous project of University of Lisbon (UL) – a Bibliometric Benchmarking Analysis of University of Lisbon, for the period of 2000-2009 – a database was created to support research information (ULSR). However this system was not integrated with other existing systems at University, as the UL Libraries Integrated System (SIBUL) and the Repository of University of Lisbon (Repositório.UL). Since libraries were called to be part of the process, the Faculty of Pharmacy Library’ team felt that it was very important to get all systems connected or, at least, to use that data in the library systems. Objectives – The main goals were to centralize all the scientific research produced at Faculty of Pharmacy, made it available to the entire Faculty, involve researchers and library team, capitalize and reinforce team work with the integration of several distinct projects and reducing tasks’ redundancy. Methods – Our basis was the imported data collection from the ISI Web of Science (WoS), for the period of 2000-2009, into ULSR. All the researchers and indexed publications at WoS, were identified. A first validation to identify all the researchers and their affiliation (university, faculty, department and unit) was done. The final validation was done by each researcher. In a second round, concerning the same period, all Pharmacy Faculty researchers identified their published scientific work in other databases/resources (NOT WoS). To our strategy, it was important to get all the references and essential/critical to relate them with the correspondent digital objects. To each researcher previously identified, was requested to register all their references of the ‘NOT WoS’ published works, at ULSR. At the same time, they should submit all PDF files (for both WoS and NOT WoS works) in a personal area of the Web server. This effort enabled us to do a more reliable validation and prepare the data and metadata to be imported to Repository and to Library Catalogue. Results – 558 documents related with 122 researchers, were added into ULSR. 1378 bibliographic records (WoS + NOT WoS) were converted into UNIMARC and Dublin Core formats. All records were integrated in the catalogue and repository. Conclusions – Although different strategies could be adopted, according to each library team, we intend to share this experience and give some tips of what could be done and how Faculty of Pharmacy created and implemented her strategy.

Future Possibilities, Social Challenges and Adaptation Requirements of Industrial Progress

The paper addresses the technological change that is currently happening in industry. First, a review of the global trends that impact industrial developmentsis made, then a summary ofexpanding intelligent technologies and their systems. The report describes in detail the concept of Industry 4.0 and its major technology-related aspects. At the end of the paper, a summary of social consequences is addressed, especially concerning generational concerns connected to the current change in industrial technology. The purpose of the study is to raise some special aspects and considerations in the given subject.

Estudo sócio-ecológico sobre tráfico de seres humanos para fins de exploração laboral na região do Alentejo : análise espacial e estatística

O presente relatório, inserido no Mestrado em Gestão do Território, Área de Especialização em Deteção Remota e Sistemas de Informação Geográfica, lecionado pelo Departamento de Geografia e Planeamento Regional da Faculdade de Ciências Sociais e Humanas da Universidade Nova de Lisboa, pretende descrever o trabalho desenvolvido pelo mestrando enquanto estagiário no Observatório do Tráfico de Seres Humanos (OTSH). O relatório está estruturado em três capítulos distintos. No primeiro capítulo é realizada uma abordagem teórica sobre o Tráfico de Seres Humanos e a distinção entre o mesmo com o Auxílio à Imigração Ilegal. Neste, é também feita uma pequena referência à problemática dos novos fluxos de refugiados/migrantes que, no momento da realização do mesmo, constituem uma questão bastante complexa sobretudo ao nível europeu. No segundo capítulo é realizada uma caracterização da área de estudo, assim como a descrição dos dados utilizados e a metodologia aplicada no mesmo. No terceiro capítulo são apresentados os resultados finais do estudo e a cartografia de síntese que sustenta os mesmos. Para a realização deste estudo recorreu-se a uma análise multicritério em SIG para prever a localização de áreas de maior suscetibilidade de ocorrência de novos casos relativos ao crime do tráfico de seres humanos para exploração laboral na agricultura, na região do Alentejo (distritos de Beja, Évora e Portalegre), através do recurso a dados estatísticos disponibilizados tanto pelo OTSH, como por outras entidades. A metodologia apresentada integra um SIG baseado num modelo raster com o Analytical Hierarchy Process (AHP). Através da realização deste estudo, a importância dos SIG como ferramenta no auxílio ao processo de tomada de decisão, pôde ser testada, conjuntamente com o processo metodológico AHP, através dos resultados apresentados. Com um possível desenvolvimento deste modelo analítico, pretende-se que o mesmo seja adaptável a outras regiões e em última instância, outros tipos de exploração e/ou tráfico.

Histórias com história dentro

Dissertação de Mestrado, Educação de 1.º e 2.º Ciclo do Ensino Básico, Escola Superior de Educação e Comunicação, Universidade do Algarve, 2016

A distributed middleware for IT/OT convergence in modern industrial environments

The modern industrial environment is populated by a myriad of intelligent devices that collaborate for the accomplishment of the numerous business processes in place at the production sites. The close collaboration between humans and work machines poses new interesting challenges that industry must overcome in order to implement the new digital policies demanded by the industrial transition. The Industry 5.0 movement is a companion revolution of the previous Industry 4.0, and it relies on three characteristics that any industrial sector should have and pursue: human centrality, resilience, and sustainability. The application of the fifth industrial revolution cannot be completed without moving from the implementation of Industry 4.0-enabled platforms. The common feature found in the development of this kind of platform is the need to integrate the Information and Operational layers. Our thesis work focuses on the implementation of a platform addressing all the digitization features foreseen by the fourth industrial revolution, making the IT/OT convergence inside production plants an improvement and not a risk. Furthermore, we added modular features to our platform enabling the Industry 5.0 vision. We favored the human centrality using the mobile crowdsensing techniques and the reliability and sustainability using pluggable cloud computing services, combined with data coming from the crowd support. We achieved important and encouraging results in all the domains in which we conducted our experiments. Our IT/OT convergence-enabled platform exhibits the right performance needed to satisfy the strict requirements of production sites. The multi-layer capability of the framework enables the exploitation of data not strictly coming from work machines, allowing a more strict interaction between the company, its employees, and customers.

Integrating Machine Learning Paradigms for Predictive Maintenance in the Fourth Industrial Revolution era

In the last decade, manufacturing companies have been facing two significant challenges. First, digitalization imposes adopting Industry 4.0 technologies and allows creating smart, connected, self-aware, and self-predictive factories. Second, the attention on sustainability imposes to evaluate and reduce the impact of the implemented solutions from economic and social points of view. In manufacturing companies, the maintenance of physical assets assumes a critical role. Increasing the reliability and the availability of production systems leads to the minimization of systems’ downtimes; In addition, the proper system functioning avoids production wastes and potentially catastrophic accidents. Digitalization and new ICT technologies have assumed a relevant role in maintenance strategies. They allow assessing the health condition of machinery at any point in time. Moreover, they allow predicting the future behavior of machinery so that maintenance interventions can be planned, and the useful life of components can be exploited until the time instant before their fault. This dissertation provides insights on Predictive Maintenance goals and tools in Industry 4.0 and proposes a novel data acquisition, processing, sharing, and storage framework that addresses typical issues machine producers and users encounter. The research elaborates on two research questions that narrow down the potential approaches to data acquisition, processing, and analysis for fault diagnostics in evolving environments. The research activity is developed according to a research framework, where the research questions are addressed by research levers that are explored according to research topics. Each topic requires a specific set of methods and approaches; however, the overarching methodological approach presented in this dissertation includes three fundamental aspects: the maximization of the quality level of input data, the use of Machine Learning methods for data analysis, and the use of case studies deriving from both controlled environments (laboratory) and real-world instances.

Security enhancements and flaws of emerging communication technologies

The multi-faced evolution of network technologies ranges from big data centers to specialized network infrastructures and protocols for mission-critical operations. For instance, technologies such as Software Defined Networking (SDN) revolutionized the world of static configuration of the network - i.e., by removing the distributed and proprietary configuration of the switched networks - centralizing the control plane. While this disruptive approach is interesting from different points of view, it can introduce new unforeseen vulnerabilities classes. One topic of particular interest in the last years is industrial network security, an interest which started to rise in 2016 with the introduction of the Industry 4.0 (I4.0) movement. Networks that were basically isolated by design are now connected to the internet to collect, archive, and analyze data. While this approach got a lot of momentum due to the predictive maintenance capabilities, these network technologies can be exploited in various ways from a cybersecurity perspective. Some of these technologies lack security measures and can introduce new families of vulnerabilities. On the other side, these networks can be used to enable accurate monitoring, formal verification, or defenses that were not practical before. This thesis explores these two fields: by introducing monitoring, protections, and detection mechanisms where the new network technologies make it feasible; and by demonstrating attacks on practical scenarios related to emerging network infrastructures not protected sufficiently. The goal of this thesis is to highlight this lack of protection in terms of attacks on and possible defenses enabled by emerging technologies. We will pursue this goal by analyzing the aforementioned technologies and by presenting three years of contribution to this field. In conclusion, we will recapitulate the research questions and give answers to them.

Wireless solutions for the industrial internet of things

The fourth industrial revolution is paving the way for Industrial Internet of Things applications where industrial assets (e.g., robotic arms, valves, pistons) are equipped with a large number of wireless devices (i.e., microcontroller boards that embed sensors and actuators) to enable a plethora of new applications, such as analytics, diagnostics, monitoring, as well as supervisory, and safety control use-cases. Nevertheless, current wireless technologies, such as Wi-Fi, Bluetooth, and even private 5G networks, cannot fulfill all the requirements set up by the Industry 4.0 paradigm, thus opening up new 6G-oriented research trends, such as the use of THz frequencies. In light of the above, this thesis provides (i) a broad overview of the main use-cases, requirements, and key enabling wireless technologies foreseen by the fourth industrial revolution, and (ii) proposes innovative contributions, both theoretical and empirical, to enhance the performance of current and future wireless technologies at different levels of the protocol stack. In particular, at the physical layer, signal processing techniques are being exploited to analyze two multiplexing schemes, namely Affine Frequency Division Multiplexing and Orthogonal Chirp Division Multiplexing, which seem promising for high-frequency wireless communications. At the medium access layer, three protocols for intra-machine communications are proposed, where one is based on LoRa at 2.4 GHz and the others work in the THz band. Different scheduling algorithms for private industrial 5G networks are compared, and two main proposals are described, i.e., a decentralized scheme that leverages machine learning techniques to better address aperiodic traffic patterns, and a centralized contention-based design that serves a federated learning industrial application. Results are provided in terms of numerical evaluations, simulation results, and real-world experiments. Several improvements over the state-of-the-art were obtained, and the description of up-and-running testbeds demonstrates the feasibility of some of the theoretical concepts when considering a real industry plant.