Computer and laboratory simulation in the teaching of neonatal nursing: innovation and impact on learning

Objectives: to evaluate the cognitive learning of nursing students in neonatal clinical evaluation from a blended course with the use of computer and laboratory simulation; to compare the cognitive learning of students in a control and experimental group testing the laboratory simulation; and to assess the extracurricular blended course offered on the clinical assessment of preterm infants, according to the students. Method: a quasi-experimental study with 14 Portuguese students, containing pretest, midterm test and post-test. The technologies offered in the course were serious game e-Baby, instructional software of semiology and semiotechnique, and laboratory simulation. Data collection tools developed for this study were used for the course evaluation and characterization of the students. Nonparametric statistics were used: Mann-Whitney and Wilcoxon. Results: the use of validated digital technologies and laboratory simulation demonstrated a statistically significant difference (p = 0.001) in the learning of the participants. The course was evaluated as very satisfactory for them. The laboratory simulation alone did not represent a significant difference in the learning. Conclusions: the cognitive learning of participants increased significantly. The use of technology can be partly responsible for the course success, showing it to be an important teaching tool for innovation and motivation of learning in healthcare.

Design and Development of a Research Framework for Prototyping Control Tower Augmented Reality Tools

The purpose of the air traffic management system is to ensure the safe and efficient flow of air traffic. Therefore, while augmenting efficiency, throughput and capacity in airport operations, attention has rightly been placed on doing it in a safe manner. In the control tower, many advances in operational safety have come in the form of visualization tools for tower controllers. However, there is a paradox in developing such systems to increase controllers' situational awareness: by creating additional computer displays, the controller's vision is pulled away from the outside view and the time spent looking down at the monitors is increased. This reduces their situational awareness by forcing them to mentally and physically switch between the head-down equipment and the outside view. This research is based on the idea that augmented reality may be able to address this issue. The augmented reality concept has become increasingly popular over the past decade and is being proficiently used in many fields, such as entertainment, cultural heritage, aviation, military & defense. This know-how could be transferred to air traffic control with a relatively low effort and substantial benefits for controllers’ situation awareness. Research on this topic is consistent with SESAR objectives of increasing air traffic controllers’ situation awareness and enable up to 10 % of additional flights at congested airports while still increasing safety and efficiency. During the Ph.D., a research framework for prototyping augmented reality tools was set up. This framework consists of methodological tools for designing the augmented reality overlays, as well as of hardware and software equipment to test them. Several overlays have been designed and implemented in a simulated tower environment, which is a virtual reconstruction of Bologna airport control tower. The positive impact of such tools was preliminary assessed by means of the proposed methodology.

Methods and tools for analysis and management of risks and regulatory compliance in the healthcare sector: the hospital at home – HaH

Changing or creating an organisation means creating a new process. Each process involves many risks that need to be identified and managed. The main risks considered here are procedural and legal risks. The former are related to the risks of errors that may occur during processes, while the latter are related to the compliance of processes with regulations. Managing the risks implies proposing changes to the processes that allow the desired result: an optimised process. In order to manage a company and optimise it in the best possible way, not only should the organisational aspect, risk management and legal compliance be taken into account, but it is important that they are all analysed simultaneously with the aim of finding the right balance that satisfies them all. This is the aim of this thesis, to provide methods and tools to balance these three characteristics, and to enable this type of optimisation, ICT support is used. This work isn’t a thesis in computer science or law, but rather an interdisciplinary thesis. Most of the work done so far is vertical and in a specific domain. The particularity and aim of this thesis is not to carry out an in-depth analysis of a particular aspect, but rather to combine several important aspects, normally analysed separately, which however have an impact and influence each other. In order to carry out this kind of interdisciplinary analysis, the knowledge base of both areas was involved and the combination and collaboration of different experts in the various fields was necessary. Although the methodology described is generic and can be applied to all sectors, the case study considered is a new type of healthcare service that allows patients in acute disease to be hospitalised to their home. This provide the possibility to perform experiments using real hospital database.

Development of a Computer-Based methodology for tolerance selection and optimization applied to the automotive sector

The design optimization of industrial products has always been an essential activity to improve product quality while reducing time-to-market and production costs. Although cost management is very complex and comprises all phases of the product life cycle, the control of geometrical and dimensional variations, known as Dimensional Management (DM), allows compliance with product and process requirements. Hence, the tolerance-cost optimization becomes the main practice to provide an effective application of Design for Tolerancing (DfT) and Design to Cost (DtC) approaches by enabling a connection between product tolerances and associated manufacturing costs. However, despite the growing interest in this topic, a profitable application in the industry of these techniques is hampered by their complexity: the definition of a systematic framework is the key element to improving design optimization, enhancing the concurrent use of Computer-Aided tools and Model-Based Definition (MBD) practices. The present doctorate research aims to define and develop an integrated methodology for product/process design optimization, to better exploit the new capabilities of advanced simulations and tools. By implementing predictive models and multi-disciplinary optimization, a Computer-Aided Integrated framework for tolerance-cost optimization has been proposed to allow the integration of DfT and DtC approaches and their direct application for the design of automotive components. Several case studies have been considered, with the final application of the integrated framework on a high-performance V12 engine assembly, to achieve both functional targets and cost reduction. From a scientific point of view, the proposed methodology provides an improvement for the tolerance-cost optimization of industrial components. The integration of theoretical approaches and Computer-Aided tools allows to analyse the influence of tolerances on both product performance and manufacturing costs. The case studies proved the suitability of the methodology for its application in the industrial field, providing the identification of further areas for improvement and refinement.

Continual learning for computer vision applications

One of the most visionary goals of Artificial Intelligence is to create a system able to mimic and eventually surpass the intelligence observed in biological systems including, ambitiously, the one observed in humans. The main distinctive strength of humans is their ability to build a deep understanding of the world by learning continuously and drawing from their experiences. This ability, which is found in various degrees in all intelligent biological beings, allows them to adapt and properly react to changes by incrementally expanding and refining their knowledge. Arguably, achieving this ability is one of the main goals of Artificial Intelligence and a cornerstone towards the creation of intelligent artificial agents. Modern Deep Learning approaches allowed researchers and industries to achieve great advancements towards the resolution of many long-standing problems in areas like Computer Vision and Natural Language Processing. However, while this current age of renewed interest in AI allowed for the creation of extremely useful applications, a concerningly limited effort is being directed towards the design of systems able to learn continuously. The biggest problem that hinders an AI system from learning incrementally is the catastrophic forgetting phenomenon. This phenomenon, which was discovered in the 90s, naturally occurs in Deep Learning architectures where classic learning paradigms are applied when learning incrementally from a stream of experiences. This dissertation revolves around the Continual Learning field, a sub-field of Machine Learning research that has recently made a comeback following the renewed interest in Deep Learning approaches. This work will focus on a comprehensive view of continual learning by considering algorithmic, benchmarking, and applicative aspects of this field. This dissertation will also touch on community aspects such as the design and creation of research tools aimed at supporting Continual Learning research, and the theoretical and practical aspects concerning public competitions in this field.

Development and application of a computer-based methodology for design for additive manufacturing of automotive components

The research project aims to improve the Design for Additive Manufacturing of metal components. Firstly, the scenario of Additive Manufacturing is depicted, describing its role in Industry 4.0 and in particular focusing on Metal Additive Manufacturing technologies and the Automotive sector applications. Secondly, the state of the art in Design for Additive Manufacturing is described, contextualizing the methodologies, and classifying guidelines, rules, and approaches. The key phases of product design and process design to achieve lightweight functional designs and reliable processes are deepened together with the Computer-Aided Technologies to support the approaches implementation. Therefore, a general Design for Additive Manufacturing workflow based on product and process optimization has been systematically defined. From the analysis of the state of the art, the use of a holistic approach has been considered fundamental and thus the use of integrated product-process design platforms has been evaluated as a key element for its development. Indeed, a computer-based methodology exploiting integrated tools and numerical simulations to drive the product and process optimization has been proposed. A validation of CAD platform-based approaches has been performed, as well as potentials offered by integrated tools have been evaluated. Concerning product optimization, systematic approaches to integrate topology optimization in the design have been proposed and validated through product optimization of an automotive case study. Concerning process optimization, the use of process simulation techniques to prevent manufacturing flaws related to the high thermal gradients of metal processes is developed, providing case studies to validate results compared to experimental data, and application to process optimization of an automotive case study. Finally, an example of the product and process design through the proposed simulation-driven integrated approach is provided to prove the method's suitability for effective redesigns of Additive Manufacturing based high-performance metal products. The results are then outlined, and further developments are discussed.

On making web accessibility more accessible: strategy and tools for social good

The world currently faces a paradox in terms of accessibility for people with disabilities. While digital technologies hold immense potential to improve their quality of life, the majority of web content still exhibits critical accessibility issues. This PhD thesis addresses this challenge by proposing two interconnected research branches. The first introduces a groundbreaking approach to improving web accessibility by rethinking how it is approached, making it more accessible itself. It involves the development of: 1. AX, a declarative framework of web components that enforces the generation of accessible markup by means of static analysis. 2. An innovative accessibility testing and evaluation methodology, which communicates test results by exploiting concepts that developers are already familiar with (visual rendering and mouse operability) to convey the accessibility of a page. This methodology is implemented through the SAHARIAN browser extension. 3. A11A, a categorized and structured collection of curated accessibility resources aimed at facilitating their intended audiences discover and use them. The second branch focuses on unleashing the full potential of digital technologies to improve accessibility in the physical world. The thesis proposes the SCAMP methodology to make scientific artifacts accessible to blind, visually impaired individuals, and the general public. It enhances the natural characteristics of objects, making them more accessible through interactive, multimodal, and multisensory experiences. Additionally, the prototype of \gls{a11yvt}, a system supporting accessible virtual tours, is presented. It provides blind and visually impaired individuals with features necessary to explore unfamiliar indoor environments, while maintaining universal design principles that makes it suitable for usage by the general public. The thesis extensively discusses the theoretical foundations, design, development, and unique characteristics of these innovative tools. Usability tests with the intended target audiences demonstrate the effectiveness of the proposed artifacts, suggesting their potential to significantly improve the current state of accessibility.

Patent translation with CAT tools and Machine Translation

The aim of this essay, which focuses on patent translation, is to compare the use of Computer-Assisted Translation (CAT) and Machine Translation (MT). During my curricular internship at a specialized-translation agency called Centro Traduzioni Imolese, I was able to practice patent translation thanks to CAT tools like SDL Trados Studio, something I have never studied at university in Forlì. Nowadays, however, Machine Translation is widely used in patent translation as well, due to the vast number of technical terms and their repetitiveness in patents, so the machine can translate automatically and rapidly all repeated terms with the same word, thanks to the use of corpora and translation memories linked to the patent field. In the first chapter I will give a definition of what a patent is, and I will introduce the concept of patent literature; afterwards, I will illustrate the differences between Computer-Assisted Translation and Machine Translation used in patent translation. In the second chapter I will translate two portions of patent 102019000018530, via the Matecat online application, translating the first part with CAT and the second part with MT, then doing the same for the second portion selected from the patent. Finally, in the third chapter, I will analyse the two translations, comparing the results in order to discover which is the more efficient method for translating patents.

Computer vision in aseptic pharmaceutical manufacturing: a deep learning-based approach to vial inspection and pack formation during a lyophilizer loading cycle

Vision systems are powerful tools playing an increasingly important role in modern industry, to detect errors and maintain product standards. With the enlarged availability of affordable industrial cameras, computer vision algorithms have been increasingly applied in industrial manufacturing processes monitoring. Until a few years ago, industrial computer vision applications relied only on ad-hoc algorithms designed for the specific object and acquisition setup being monitored, with a strong focus on co-designing the acquisition and processing pipeline. Deep learning has overcome these limits providing greater flexibility and faster re-configuration. In this work, the process to be inspected consists in vials’ pack formation entering a freeze-dryer, which is a common scenario in pharmaceutical active ingredient packaging lines. To ensure that the machine produces proper packs, a vision system is installed at the entrance of the freeze-dryer to detect eventual anomalies with execution times compatible with the production specifications. Other constraints come from sterility and safety standards required in pharmaceutical manufacturing. This work presents an overview about the production line, with particular focus on the vision system designed, and about all trials conducted to obtain the final performance. Transfer learning, alleviating the requirement for a large number of training data, combined with data augmentation methods, consisting in the generation of synthetic images, were used to effectively increase the performances while reducing the cost of data acquisition and annotation. The proposed vision algorithm is composed by two main subtasks, designed respectively to vials counting and discrepancy detection. The first one was trained on more than 23k vials (about 300 images) and tested on 5k more (about 75 images), whereas 60 training images and 52 testing images were used for the second one.

Quantum Dots As Biophotonics Tools.

This chapter provides a short review of quantum dots (QDs) physics, applications, and perspectives. The main advantage of QDs over bulk semiconductors is the fact that the size became a control parameter to tailor the optical properties of new materials. Size changes the confinement energy which alters the optical properties of the material, such as absorption, refractive index, and emission bands. Therefore, by using QDs one can make several kinds of optical devices. One of these devices transforms electrons into photons to apply them as active optical components in illumination and displays. Other devices enable the transformation of photons into electrons to produce QDs solar cells or photodetectors. At the biomedical interface, the application of QDs, which is the most important aspect in this book, is based on fluorescence, which essentially transforms photons into photons of different wavelengths. This chapter introduces important parameters for QDs' biophotonic applications such as photostability, excitation and emission profiles, and quantum efficiency. We also present the perspectives for the use of QDs in fluorescence lifetime imaging (FLIM) and Förster resonance energy transfer (FRET), so useful in modern microscopy, and how to take advantage of the usually unwanted blinking effect to perform super-resolution microscopy.

Desenvolvimento de ferramentas de análise de desempenho de atletas em 3 diferentes modalidades : Handebol, Provas Combinadas e Salto com Vara

Universidade Estadual de Campinas. Faculdade de Educação Física

O uso de softwares para estimulação da percepção do surdo frente aos parametros de velocidade do ritmo : proposta de utilização do Bpm Counter e do Vpm Counter no programa de atividade ritmicas adaptado as pessoas surdas

Universidade Estadual de Campinas . Faculdade de Educação Física

Pre-processing for noise detection in gene expression classification data

Due to the imprecise nature of biological experiments, biological data is often characterized by the presence of redundant and noisy data. This may be due to errors that occurred during data collection, such as contaminations in laboratorial samples. It is the case of gene expression data, where the equipments and tools currently used frequently produce noisy biological data. Machine Learning algorithms have been successfully used in gene expression data analysis. Although many Machine Learning algorithms can deal with noise, detecting and removing noisy instances from the training data set can help the induction of the target hypothesis. This paper evaluates the use of distance-based pre-processing techniques for noise detection in gene expression data classification problems. This evaluation analyzes the effectiveness of the techniques investigated in removing noisy data, measured by the accuracy obtained by different Machine Learning classifiers over the pre-processed data.

AcMus: an open, integrated platform for room acoustics research

This article describes the design, implementation, and experiences with AcMus, an open and integrated software platform for room acoustics research, which comprises tools for measurement, analysis, and simulation of rooms for music listening and production. Through use of affordable hardware, such as laptops, consumer audio interfaces and microphones, the software allows evaluation of relevant acoustical parameters with stable and consistent results, thus providing valuable information in the diagnosis of acoustical problems, as well as the possibility of simulating modifications in the room through analytical models. The system is open-source and based on a flexible and extensible Java plug-in framework, allowing for cross-platform portability, accessibility and experimentation, thus fostering collaboration of users, developers and researchers in the field of room acoustics.