SARS-CoV-2/2019-nCoV (COVID-19), was declared by the World Health Organization as an international pandemic, on March 11, 2020. This work aims to share procedures and best practices in CT, adopted in the context of a COVID-19 protocol established in the radiology department of the CHUC (HUC), as well as the characterization of the sample of patients evaluated as suspected of the COVID-19. A retrospective study was carried out of the CT exams performed during the month of March and April 2020, for which the protocol was activated. After analysis, we found that 70% of individuals underwent an exam between 6 pm and 12 am. In the total sample, the percentage of males was higher than that of females, with individuals distributed between the age group between fifty and eighty years, and the most performed examination was the brain CT scan. The General Hospital (HG) being the assistance structure dedicated to SARS-CoV-2/2019-nCoV (COVID-19) and the HUC being the general emergency assistance structure, which saw a global reduction in its activity, and a consequent decrease the number of CT scans performed, related to the epidemiological phase. Despite these circumstances, we found that the number of CT scans performed increases with the demand for emergency services, failing to establish a relationship with the pandemic once, that 90% of the CT scans performed were related to non-COVID-19 disease. The involvement of the entire team of Radiology Technicians contributed to the harmonization and optimization of procedures, minimizing risks, and ensuring the safety of patients and professionals.

This study aimed to identify waste in organizational processes, along with innovation processes that are guided by process optimization methods and organizational sustainability policies that influence business competitiveness. When seeking to increase their competitiveness, organizations are often less concerned about adjusting existing internal processes than they are focused on technological innovation, and sustainability. To demonstrate the need for sustainable processes, the objective of this project was to reduce the amount of paper printing at a business by about 30%. The research consisted of a case study, which took place throughout 2019. In the first semester, data was collected, while in the second semester, optimization and environmental awareness training began through the project “Less Means More.” The results show that technological innovation is a source of competitive advantage, but management practices that are oriented toward sustainability and process innovation contribute to the improvement of a company’s competitiveness. The results obtained exceed the target of a 30% reduction in paper printing, reaching a 31% reduction in December 2019 compared to January 2019. Future analyses can be extended to other business processes, as well as to other parts of businesses. Monetary incentives or awards given to employees could also be introduced with a view to motivating for the need and advantages of innovation.

In October 2005, FIA (International Automobile Federation) proposed a set of rules, for the 2008/9 Formula 1 Championship, with the objective of decreasing the difficulties for overtaking due to the wake generated by the leading car. The proposal is based on the utilization of a new concept of a double rear wing "CDG-wing" (Central Downwash Generating Wing). The objectives of this work is to analyze the aerodynamic characteristics of the CDG wing proposed by FIA, and to investigate its better positioning relative to the rear wheel. The study was performed using three different geometries of the wing, and three different speeds, to reproduce three different curves of some race tracks used in Formula 1. The computational tests have shown that the location of the wings behind the rear tire increases the downforce.

Electrical Power Systems recognized as vast and complex entities, are the true ”engine” of contemporary societies. Currently, social and environmental concerns dominate the political agenda, notably highlighted by the Paris Agreement (2015), the EU’s Energy Roadmap 2050, and the UN’s 2030 Agenda for Sustainable Development. These initiatives stem from the detrimental impacts of persistent greenhouse gas emissions, requiring a significant contribution from these entities. This scenario foreshadows a substantial increase in renewable energy penetration, leading to increasingly decentralized and variable generation. Consequently, the Iberian Power Systems, as a leading adopter, faces additional and considerable transformations, requiring a reevaluation of otherwise outdated predicates and in more micro level, decisionmaking processes. Key areas demanding special attention include the future energy policy, which can only be effectively shaped after reviewing the evolution of the power generation structure within a market-integrated context, as well as the multitude of related options for providing system flexibility—a key enabler of this profound change. Moreover, a prevailing trend across nearly every field is the increasing reliance on data proliferation, machine learning models, and advanced optimization tools to address challenges that were once considered highly complex and less accessible. This is particularly evident in the context of electrical power systems design and operation, where such tools are now being applied to solve micro-level tasks with significant accuracy. As a result, to effectively integrate these tools, it is essential to first delve into the theoretical bedrock and practical intricacies — including the selection and tuning of hyperparameters, model architectures, and optimization strategies. This knowledge has become indispensable for modern power systems engineers, enabling them to better understand and tackle the micro-level challenges that often stem from broader energy policy decisions. In this context, the optimal power flow and short-term price forecasting problems — both of vital importance to electricity market participants — will serve as experimental test beds to rigorously evaluate and demonstrate the application of these data-driven methodologies.

Ethanol production from lignocellulosic material includes three major steps: biomass pretreatment, which fragments the lignocellulosic matrix to facilitate the enzymes access to the substrate; hydrolysis, where the polysaccharides are converted into fermentable sugars (e.g. glucose and xylose); and finally, fermentation that produces ethanol or other biologically based chemicals (e.g. lactic acid, succinic acid). The aim of the present work was to study the effect of some operative variables of the pretreatment stage, namely sodium h ydrogen sulfite and sulfuric acid loadings, temperature and time, on the release of sugars in enzymatic hydrolysis, performed applying a Novozymes® cocktail, with fixed charges and operating conditions. Cystisus striatus wood branches were chipped and submitted to different reaction conditions, with a central composite experimental design 2^4+star, exploring the following variables: sulfuric acid charge (0-3%, on wood), sodium bisulfite charge (0-4 %, on wood), maximum temperature (150-190ºC) and time at maximum temperature (0-30 minutes). After pretreatment, the acid hydrolysates were recovered and the solid residues were mechanically disintegrated and thereafter subjected to enzymatic hydrolysis with an enzymatic cocktail from Novozymes®. Sugars and by-products released in the sulfite pretreatment and enzymatic treatment hydrolysates were analyzed by HPLC. The percentage of material released in the acid hydrolysates was between 7 and 29.1%. Temperature and sulfuric acid load were the most important tested variables. The inhibition products represented less than 0.9% of the initial wood mass, even for the most severe reaction conditions. Enzymatic hydrolysis of polysaccharides on solid residues resulted in conversions from 6.0 to 68.9%, depending on the reaction conditions used in biomass pretreatment with sulfite and sulfuric acid. The rate of sugars release proved to be high at the beginning, gradually decreasing with contact time. The experimental data analysis using Statgraphics®Plus5 enabled us to obtain correlation expressions and conclude that all study variables influence this phase of the process, mainly temperature and sulfuric acid load. For a given sulfite load, more acidic conditions led to higher sugar release and greater fragmentation of the material, but also higher production of degradation products; moderate sodium bisulfite (1%) and sulfuric acid (2.25%) loads released practically all hemicelluloses in the raw material.

A COFICAB é uma empresa internacional especializada na produção de cabos elétricos para a indústria automóvel e setores relacionados, tendo como principal matéria-prima o cobre. Devido à complexidade do fluxo de produção no chão de fábrica, o planeamento e sequenciamento da produção tornam-se desafios significativos, exigindo estratégias eficientes para otimizar os processos e garantir o cumprimento dos prazos de entrega. O presente documento descreve a investigação e desenvolvimento de um algoritmo de otimização para o planeamento e sequenciamento da produção, focado nos três principais processos da área do metal: desbastagem, trefilagem e torção. O objetivo é facilitar o trabalho das equipas de logística e planeamento da COFICAB, proporcionando maior eficiência na alocação de recursos e facilitando a tomada de decisões. O algoritmo desenvolvido é integrado numa aplicação web, permitindo a visualização interativa dos resultados através de um gráfico de Gantt. O primeiro passo rumo a este objetivo consiste na identificação dos principais desafios operacionais enfrentados no chão de fábrica da COFICAB, bem como das restrições existentes no planeamento da produção. Em seguida, a realização de uma revisão bibliográfica permite aprofundar o conhecimento sobre os problemas subjacentes, e analisar abordagens e metodologias aplicáveis à complexidade do contexto industrial da empresa. Complementando o estado da arte, a abordagem proposta neste trabalho é delineada de forma detalhada, assim como a definição dos requisitos técnicos e funcionais em colaboração com as equipas da COFICAB, a modelagem do sistema, a estrutura de dados e a arquitetura do sistema, que se divide em três componentes principais: base de dados, servidor e interface web. Após a fase de planeamento, procede-se ao desenvolvimento e implementação da solução. Durante esta fase, são definidos e detalhados os critérios que influenciam os resultados do algoritmo, incluindo o seu fluxo de execução e a respetiva integração numa aplicação web, garantindo o cumprimento dos requisitos estabelecidos. Para avaliar a eficácia da solução proposta, o algoritmo é submetido a um conjunto de testes práticos, realizados em colaboração com as equipas de logística e planeamento. Estes testes visam simular um cenário real de planeamento, permitindo uma análise crítica dos resultados obtidos e a validação da sua aplicabilidade no ambiente de produção da COFICAB. Por fim, são apresentadas as principais conclusões, incluindo a validação do trabalho desenvolvido, as dificuldades enfrentadas e os objetivos alcançados. O documento encerra com a definição de possíveis melhorias e futuras implementações, visando a evolução contínua da solução proposta.

Mass is a key factor during the design of an aircraft. Since the wing is one of the heaviest components, an accurate prediction of its mass is essential for a correct definition of its size and evaluation of the aircraft’s performance. Composite materials usually compose aircraft structures due to their high specific strength and ease of manufacture. By finding the optimal stacking sequence and layer orientation, it is possible to minimize the mass of components ensuring that constraints such as structural integrity and maximum displacement are fulfilled. Many studies show that Evolutionary Algorithms combined with Finite Element Analysis is a good approach to optimize composite structures regarding layer composition and orientation. This thesis describes the development of a tool that performs the structural optimization of Unmanned Aerial Vehicles’ wings made of composite structures. The motivation for this work lies in the need to improve the structural sizing method used by the University of Beira Interior teams during the design phase for the Air Cargo Challenge competition. Typically, the wing is a two-cell beam structure with sandwich skin, spar webs and laminated spar caps. In the computational model, triangular plate elements are used to represent both spar webs and the skin, and bar elements define the spar caps. For this structure, the stacking sequence of the skin must be found as well as the number of layers of each spar cap for minimum mass subject to failure and deflection (wing tip deflection and twist) constraints. To generate the mesh, the wings’ cross-section is divided into five sub-sections: leading-edge, upper and lower surfaces between spar positions and leading and rear spar webs. Based on the number of divisions and the spacing technique chosen for each sub-section, the section nodes for the structural problem are computed. Since the number of divisions and spacing technique of the cross-section are kept constant across the span, the panel nodes are the result of the interpolation of the section nodes between the extreme sections of each panel. After the node numbering process, the mesh is defined panel-by-panel, generating triangular elements for the panel skin and webs of both spars and linear elements for the spar caps. The loads are computed using the lifting line theory and transferred to the mesh considering that they are applied on the web and caps of the main spar. The solution uses MYSTRAN as the finite element solver to assess failure criteria, and the Simple Genetic Algorithm from OpenMDAO to solve the integer optimization problem. Typically, the materials considered for the problem are orthotropic, including unidirectional and bidirectional fabrics. Manufacturing constraints are addressed considering symmetrical sandwich structures, in which the core is the central layer, and by orienting the unidirectional fabric of the spar caps with the longitudinal direction of the panel. The maximum number of layers for each structure and the orientations in which a fabric can be applied must be specified by the user. To reduce the number of design variables, a database containing all possible arrangements of layer’s material and orientation for each structure is generated. This method allows to fix the number of design variables per panel to seven: stacking sequences of shell, main and rear spar webs, and number of layers of each spar cap. Since this is a high demanding computational optimization process, the parallel processing option available at OpenMDAO is activated, allowing to simultaneously analyze more than one individual of a generation. As a case study, the central panel of the 2019 Air Cargo Challenge aircraft wing from AERO@UBI is optimized, testing the optimization tool. A reduction of 16.5% on the panel’s mass is achieved during the simulations. From the different optimization settings tested it is considered that a mutation rate of 0.05, population size of 30 individuals and 20 generations is the combination that best suit this optimization problem. From the results obtained, it is recommended to implement an additional constraint able of measuring the difference between the deformed and the original shapes to prevent excessive aerodynamic losses.

The aerodynamics of oscillating airfoils are crucial to understanding subjects such as rotor dynamics and bio-inspired flows. Unsteady airfoils have been studied extensively, but there is an overall lack of knowledge regarding newer and more complex kinematics. The present paper builds upon our modified version of the NACA0012 by numerically comparing its way of flapping with the standard flapping that is common in the literature. The comparison is conducted parametrically at a Reynolds number of 104 for two nondimensional amplitudes. Then, using a gradient-based optimization method, we search for pitching amplitudes that maximize the propulsive power and efficiency for both flapping modes. Results indicate that the proposed flapping methodology is more promising than conventional flapping, with thrust increases up to approximately 40%. Furthermore, the proposed mechanism achieves maximum propulsive power with near-optimal efficiency, a common limitation of traditional flapping airfoils.

This study was undertaken to find the optimum conditions of a new enzymatic process to remove plant residues from wool. Commercial enzymatic preparations of Celluclast 1.5 L and Pectinex Ultra SP-L were selected in order to hydrolyze the polysaccharides in primary plant cell walls and middle lamella, resulting into more fragile residues easier to be removed. Since it was intended to define the optimal conditions for enzyme application, a four-factor central composite design was selected to study the effects of pH, temperature, enzyme concentration and wetting agent concentration, on the two selected responses, i.e., soluble reducing sugars (RS) and alkali solubility (AS) of wool to detect plant degradation and to evaluate wool quality, respectively. Results demonstrated that enzyme concentration was the most significant effect in plant residues degradation. A total enzyme concentration loading of 20 mL of both diluted enzymatic preparations in equal parts per 1 L of incubation solution (42.970 U/L of Celluclast preparation and PG 29.3 nkat/L + PME 2.537 nkat/L of Pectinex preparation), yielded an equivalent amount of 240.127 mg of glucose per 1.0 g of plant residue, at the optimal conditions: 40.56 ◦C, pH 4.0 and 1 mL Plurafac/L. SEM analysis has indicated an identical and important degradation of the plant residues, when compared to the conventional carbonization process, and wool quality has been preserved.

Nowadays, agriculture deals with intensive production, plagues, pests and inefficient use of natural and energy resources, also due to global temperature increase and climate change. This work seeks to answer the need to create new decision support tools for the application of phytopharmaceuticals. Mathematical objective functions capable of answering two problems were gathered. The first simulates the route and life cycle of a phytopharmaceutical. The second objective function provides the ideal quantities of phytopharmaceutical to be applied, based on climatic conditions, temperature, size of the tree canopy and dimensions of the agricultural fields.