Meat yield of Bolinus brandaris (Gastropoda: Muricidae): comparative assessment of the influence of sex, size and reproductive status

The present study assessed the influence of sex, size and reproductive status on the meat yield (soft tissues proportion) of the purple dye murex (Bolinus brandaris) from the Ria Formosa lagoon (southern Portugal). During one year of monthly sampling (October 2008-September 2009), average meat yield of B. brandaris was 40.5 +/- 6.1% (range: 25.8-56.1% wet weight), with no significant differences between sexes. Relationships established between specimen size and soft parts weight indicated that both shell length and total weight are excellent indicators of meat yield. Significant differences in meat yield between size classes further reinforced the trend of increasing meat yield during ontogeny. Meat yield exhibited significant monthly variation and a similar temporal trend in both sexes, which were directly related to the reproductive status. Meat yield of B. brandaris was compared with that of other muricid species and the marked influence of the reproductive status on meat yield prompted a comparative assessment of the spawning season and peak of three sympatric muricids (B. brandaris, Hexaplex trunculus and Stramonita haemastoma). Overall, these findings have implications at diverse levels, including the management, regulation and inspection of this fishing/ harvesting activity and the commercialization and consumption of this seafood product.postdoctoral grant [SFRH/BPD/26348/2006]; Fundacao para a Ciencia e Tecnologia (FCT - Portugal); Fisheries Operational Programme (PROMAR); European Fisheries Fund [EFF 2007-2013]info:eu-repo/semantics/publishedVersio

Desenvolvimento de um modelo de comportamento elastoplástico através de inteligência artificial

In the past few years, there has been tremendous advances in the accuracy and predictive capabilities of tools for the simulation of materials. Predictive modeling has now become a powerful tool that can also deliver real value through application and innovation to the global industry. Simulation of forming operations, particularly using the nite element method, is clearly dependent on the accuracy of the constitutive models. In the last years, several methodologies were developed to improve the accuracy of constitutive models through parameter identi cation and calibration methodologies. However, independently of the e cacy of the calibration methods, the accuracy of a constitutive model is always constrained to its prede ned mathematical formulation. Additionally, using known elastoplastic formulations, it is impossible to reproduce the material phenomena if these phenomena are not formulated mathematically. In the past several years, arti cial intelligence (AI) techniques have become more robust and complex. This eld has set the ambitious goal of making machines either seemingly or genuinely intelligent. The sub- eld of arti cial intelligence known as machine learning attempts to make computers learn from observations. Machine-learning algorithms are general tools that can be tted to a vast number of problems, including predicting the stress-strain relationship of the material. This work proposes to model the behavior of a metal material using machinelearning (ML) techniques and use this ML in forming simulations. Initially, the ML model is designed and trained using a known plane stress elastoviscoplasticity model to evaluate its competence to replace classical models. Di erent ML topologies and optimization techniques are used to train the model. Then, the AI model is introduced into a nite element analysis (FEA) code, as a user subroutine, and its attainment in forming simulations is evaluated. The replacement of classical formulations by AI techniques for the material behavior de nition is analysed and discussed.Nos últimos anos, tem havido enormes avanços na precisão e capacidades preditivas de ferramentas para a simulação de materiais. A modelação preditiva tornou-se numa ferramenta poderosa que também pode agregar um grande valor por meio de aplicações e inovações para a indústria global. A simulação das operações de conformação, particularmente usando o método dos elementos finitos, é claramente dependente da precisão dos modelos constitutivos. Nos últimos anos, várias metodologias foram desenvolvidas para melhorar a precisão de modelos constitutivos através de metodologias de identificação e calibração de parâmetros. No entanto, independentemente da eficácia dos métodos de calibração, a precisão de um modelo constitutivo é sempre restrita a sua formulação matemática predefinida. Adicionalmente, usando formulações elastoplasticas conhecidas, e impossível reproduzir os fenomenos do comportamento de materiais se estes comportamentos não forem eficazmente formulados matematicamente. Recentemente, as tecnicas de inteligencia artificial (IA) tornaram-se mais robustas e complexas. Este campo estabeleceu o objetivo ambicioso de tornar as maquinas aparentemente ou genuinamente inteligentes. O sub-campo da inteligencia artificial conhecido como aprendizagem computacional tenta fazer com que os computadores aprendam com as observações. Os algoritmos de aprendizagem computacional são ferramentas gerais que podem ser adaptadas a um grande numero de problemas, incluindo a previsão da relação tensao-deformação do material. Este trabalho propõe modelar o comportamento de um material metalico utilizando tecnicas de aprendizagem computacional (ML) e utilizar este ML na modelação de simulações. Inicialmente, o modelo ML e projetado e treinado usando um modelo de elastoviscoplasticidade em estado plano de tensão de forma a avaliar a sua eficacia na substituição de modelos classicos. Diferentes topologias ML e tecnicas de otimização são usadas para treinar o modelo. Em seguida, o modelo IA e introduzido num codigo de analise de elementos finitos (FEA), como user subroutine, e a sua concretização em simulações de conformação e avaliada. A substituição de formulações classicas por tecnicas de IA para a definiçao do comportamento do material e analisada e discutida.Mestrado em Engenharia Mecânic

A methodological approach in order to support decision-makers when defining Mobility and Transportation Politics

Nowadays Portugal is under a large process of creation/revision of studies and plans related with land use and territorial planning, mainly due to the end of the lifetime period of the actual Municipal Master Plan, but also because of the creation of the new Metropolitan Authorities of Transportation, which will require Mobility Plans. Even though the Portuguese law doesn’t impose these Mobility Plans at the present moment, there is a general feeling about the importance of the mobility system for the society and economics in general. This is the case in highly density areas, where the need and complexity of the system requires these specific studies in order to obtain an efficient management; or in the case of low-density areas where the risk of loosing competitiveness is too high to ignore the importance of the transportation and mobility system, and the advantage of gaining local and regional competitiveness might increase the importance of the municipality in regional context. This paper intends to provide an innovative approach regarding the provision, at an early stage, of technical support to decision-makers in order to define Mobility and Transportation Policies. The opportunity provided by using adapted SWOT analysis (among others) to identify weakening or potential factors, and how to take advantage of the results, always using a cause and effect approach and a coherent policy in order to obtain high quality and effective studies and politics. The methodology relies on a two-stage process. In the first stage a summary diagnose is provided, using inputs which are supposed to well characterise the territory’s mobility patterns. Afterwards, in a second phase, these are inter-related and evaluated in order to build-up a table of options, where policies are proposed with a careful attention to its qualitative cross impact with the measures and objectives intended to be achieved. The proposed methodology was applied in the Alcobaça´s Municipality case study, which provided different lines of action in diverse subjects, such as, public and private transportation networks, parking policies and organisation, and territory competitiveness. This study was particularly relevant, since this Municipality is under great pressure of its neighbour municipalities, has a low level of regional importance and a low intra-municipal cohesion. Finally, the general opinion of the decision-makers about this technical approach is presented. Keywords: Mobility; Transportation; Land Planning and Policies; Decision-making Support

Desenvolvimento e otimização de um dispositivo aerodinâmico para o carro do Formula Student UA

The development and optimization of the rear wing of a Formula Student car must be done with the aid of CFD numerical simulations, since in order to ensure a good aerodynamic performance a great number of wing configurations need to be tested. The aim of this thesis was to develop a fully functional optimization code, that could be easily adapted to generate the optimal rear wing for any given Formula Student car, only needing the car CFD results. As a means to accomplish that, a CFD simulation was performed to the Formula Student Aveiro teams’ car and additional wind tunnel testing was conducted with the purpose of corroborating the simulation results. Hereupon, the velocity profile at the car rear end, obtained in the CFD simulation, was used as the inlet in the rear wing simulation for the optimization process, allowing a contribution of the car geometry to the rear wing optimization without the addition of unnecessary computational time. Finally, an optimization code based on the Harmony Search Algorithm was created to define the optimal rear wing parameters and with that achieve an optimized rear wing configuration. The optimized configuration consists of 4 airfoils, and showed excellent results even surpassing the rear wing performance of the 2016 FSAE Czech Republic competition winner.O desenvolvimento e otimização da asa traseira de um carro do tipo Formula Student devem ser tratados através de simulações numéricas do tipo CFD, dado que, para assegurar uma boa performance aerodinâmica da asa traseira, teriam de ser testados um grande número de configurações. O objetivo deste trabalho era desenvolver um código de otimização¸ completamente funcional, capaz de ser facilmente adaptado, de forma a gerar uma asa traseira ótima para qualquer veículo do tipo Formula Student, sendo apenas necessários os resultados da simulação CFD. De forma a cumprir o proposto, foi realizada ao carro da equipa de Fórmula Student da UA uma analise CFD, tendo, adicionalmente, sido efetuados testes no túnel de vento com o propósito de corroborar os resultados da simulação. Tendo em consideração o exposto, o perfil de velocidades na parte traseira do carro, obtido através da sua simulação CFD, foi usado como Inlet na simulação da asa traseira para o processo de otimização, permitindo a contribuição da geometria do carro para o processo de otimização da asa traseira sem a adição de tempo computacional desnecessário. Por fim, foi criado um código de otimização baseado no Harmony Search Algorithm, com o propósito de otimizar os parâmetros que definem a geometria da asa traseira e com isso obter com uma configuração otimizada. A configuração otimizada e composta por 4 airfoils, tendo demonstrado excelentes resultados, ultrapassando até o desempenho da asa traseira da equipa que ganhou a competição FSAE Czech Republic, em 2016.Mestrado em Engenharia Mecânic

Embedded real-time vision-based control and inspection of an industrial process

This era is often called the age of data science. New sources of knowledge, technology, and data sources frequently flood society’s daily life in all shapes and forms. At a time when we have companies like Amazon that can accurately predict the products we want to buy, or companies like Netflix that know which possible movies might interest us, we find ourselves surrounded more and more by intelligent algorithms. Every day, we look for more ways to improve these algorithms so that they can help humans with their tasks and, in some cases, even replace them. In industry, more and more functions that in the past were the sole responsibility of human labor are being taken over by machines. Despite all the ethical and moral questions this may raise, the improvement in efficiency and productivity is undeniable. Machines don’t get tired, sick or take lunch breaks. What’s more, the margin for error is minimal. All processing lines inevitably create defective products. These defective products will travel down the assembly line, wasting money, time and resources, which presents a problem for the industry. This work is based on studying a solution to this problem by studying the use of Machine Learning in an industrial process, to carry out quality inspection and controlling the system behaviour using vision-based systems. It will be a vision module for computer vision tasks that will be integrated with the implementation of a simulation kit. In this document, we will carry out a literature review on the most relevant topics, and a short analysis of the challenges and problems while developing the work. A suggested framework will also be presented, with all its advantages and analyses, and a description will be given of the system’s implementation and the results obtained.Esta era é frequentemente designada como a era da ciência dos dados. Novas fontes de conhecimento, tecnologia e fontes de dados inundam frequentemente o quotidiano da sociedade de todas as formas e feitios. Numa altura em que temos empresas como a Amazon que conseguem prever com precisão os produtos que queremos comprar, ou empresas como a Netflix que sabem quais os filmes que nos podem interessar, estamos cada vez mais rodeados de algoritmos inteligentes. Todos os dias, procuramos mais formas de melhorar estes algoritmos para que possam ajudar os humanos nas suas tarefas e, nalguns casos, até substituí-los. Na indústria, cada vez mais funções que no passado eram da exclusiva responsabilidade do trabalho humano estão a ser assumidas por máquinas. Apesar de todas as questões éticas e morais que este facto possa levantar, a melhoria da eficiência e da produtividade é inegável. As máquinas não se cansam, não adoecem, não fazem pausas para almoço. Para além disso, a margem de erro é mínima. Todas as linhas de processamento criam inevitavelmente produtos defeituosos. Estes produtos defeituosos percorrerão a linha de montagem, desperdiçando dinheiro, tempo e recursos, o que constitui um problema para a indústria. Este trabalho baseia-se no estudo de uma solução para este problema, estudando a utilização da Aprendizagem Automática num processo industrial, para realizar a inspeção da qualidade e controlar o comportamento do sistema utilizando sistemas baseados na visão. Será um módulo de visão para tarefas de visão computacional que será integrado com a implementação de um kit de simulação. Neste documento, faremos uma revisão da literatura sobre os tópicos mais relevantes, e uma breve análise dos desafios e problemas durante o desenvolvimento do trabalho. Será também apresentada uma estrutura sugerida, com todas as suas vantagens e análises, e será feita uma descrição da implementação do sistema e dos resultados obtidos

Anticancer peptides : prospective innovation in cancer therapy

© Springer International Publishing Switzerland 2016Current cancer treatments require improvements in selectivity and efficacy. Surgery, radiation, and chemotherapy approaches result in patient’s suffering over time due to the development of severe side-effects that simultaneously condition adherence to therapy. Biologically active peptides, in particular antimicrobial peptides (AMPs), are versatile molecules in terms of biological activities. The cytotoxic activities of several AMPs turn this group of molecules into an amazing pool of new templates for anticancer drug development. However, several unmet challenges limit application of peptides in cancer therapy. The mechanism(s) of action of the peptides need better description and understanding, and innovative targets have to be discovered and explored, facilitating drug design and development. In this chapter, we explore the natural occurring AMPs as potential new anticancer peptides (ACPs) for cancer prevention and treatment. Their modes of action, selectivity to tumor compared to normal cells, preferential targets, and applications, but also their weaknesses, are described and discussed.info:eu-repo/semantics/publishedVersio

Equity Research Report - Carrefour Group

Mestrado Bolonha em FinançasThe Carrefour Group is “one of the world’s leading food retailer’s” that operates worldwide with some value chain elements integrated, including warehousing and food processing. In 2020FY, Carrefour had total assets of €47.59Bn, net sales of €70.72Bn and a market capitalization of €11.47Bn. The Group has a Buy recommendation, where the 2022YE PT is €19.17/share against the closing price of €15.18/share on September 13th, 2021 which corresponds to an upside potential of 26.3% considering a medium risk level. The reason for this undervaluation was the warning of a profit loss in 2017 which led to a share price drop of 27.4% between May and August and the change of CEO. Since then, there were other minor events that scared the investors, but the Management is confident in the recent results and, during 2021, announced two share buybacks. The food retail industry is an essential industry, so in times of recession or growth, the changes are moderate. Also, it is a mature industry, so the players have to follow or create trends to compete, and Carrefour is following those trends like e-commerce where the Group earned GMV of €2.3Bn. Other trend is the health concerns that consumers have, and Carrefour has the plan of offering healthy, with quality and sustainably produced food. Carrefour is a company that, even in times of negative earnings, kept its commitment with shareholders. The company is slowly improving and is expected that profit will rise 9.61% CAGR 2020-2025F and dividend per share to rise 10.9%.O Carrefour Group é “um dos líderes no retalho alimentar” e tem operações por todo o mundo com alguns elementos da cadeia de valor integrados, incluindo armazenamento e processamento alimentar. Em 2020, o Carrefour tinha um total de ativos no valor de €47.59 biliões, vendas de €70.72 biliões e uma capitalização bolsista de €11.47 biliões. O grupo tem uma recomendação de investimento de Compra, com um preço alvo de €19.17/ação no final de 2022 contra o preço de fecho de €15.18/ação no dia 13 de Setembro de 2021, o que corresponde a um potencial de valorização de 26.3% considerando um nível médio de risco. A razão para esta subvalorização está relacionada com um aviso de perda nos lucros em 2017 o que levou a uma queda no valor das ações de 27.4% entre Maio e Agosto e a troca de CEO. Desde então, houve acontecimentos menores que afastaram acionistas, mas administração do Carrefour estão confiantes nos resultados mais recentes e durante 2021 anunciaram dois programas de recompra de ações. A indústria do retalho alimentar é considerada essencial, por isso em tempos de recessão ou crescimento económico, as alterações são moderadas. Também é uma indústria em estado de maturação por isso os competidores têm de seguir ou criar tendências para continuarem no mercado, e o Carrefour tem seguido essas tendências como o e-commerce onde o grupo ganhou um GMV de €2.3 biliões. Outra tendência são as preocupações de saúde que os consumidores têm e o Carrefour criou um plano para oferecer comida saudável, com qualidade e sustentável. O Carrefour é uma empresa que, mesmo em tempo de prejuízo, sempre cumpriu os seus compromissos com os acionistas. Eles têm tido um crescimento lento e é esperado que os lucros aumentem 9.61% e os dividendos por ação 10.9%.info:eu-repo/semantics/publishedVersio

Mate Competition Drives Aggressive Behaviour in Female Dosophila

"Aggression is an adaptive set of behaviours that allows animals to compete against one another in an environment of limited resources. In Drosophila such aggressive behaviour has been extensively studied in males. Despite recent work highlighting territorial defence in females, female aggression in Drosophila is still poorly understood. Indeed, whether females compete for mating partners, as males do, has remained unknown so far. In this thesis, we report that Drosophila melanogaster females reliably display aggression towards mating pairs although without any bearing on either the aggressor’s or the target’s fertility or fecundity.