Descriptive analysis of the impact of family business's in the Portuguese context: AEF universe

This study aims to produce an analysis of the universe of associates belonging to the Portuguese Association of Family Firms. The goal is not only to characterize them, but also to establish comparisons between them and the Portuguese macroeconomical scenario. The beginning of this study is an introduction to the main topics surrounding the scope of family businesses that are currently being investigated in the academia. In order to complete the framing of the subject and the produced analysis, a summary of what has been debated on this topic was made, emphasizing the concerns, challenges and tendencies that this type of companies has felt. The confrontation between these topics and the results obtained will be the target of the final discussion. It should be noted that the results and analyzes produced were based on data from 464 companies. That data was collected with the goal of producing indicators capable of allowing a descriptive analysis of the firms and also the macroeconomic impact they have on a national level. In order to comply with the goals of the study, the indicators generated were produced to provide an image that characterizes the sectors of activity as well as the legal form, geographical location, size and performance for the firms in study. The data covers the time period that goes from 2010 to 2016. Finally, in terms of macroeconomic impact, the analysis carried by this study focus on topics such as Corporate Income Tax, Personnel Costs and Gross Domestic Product.Com este estudo pretende-se realizar uma análise ao universo de associados da Associação das Empresas Familiares de forma a caracterizá-los e estabelecer algumas comparações com o panorama nacional português. No entanto, surge como principal objetivo do estudo, a elaboração de alguns indicadores que ajudem a revelar o impacto que este universo tem no contexto nacional. A abordagem inicial ao tema é iniciada com uma análise aos principais temas em debate no universo académico e de investigação relativamente à temática das empresas familiares e do crescente interesse no estudo da mesma. De forma a completar o enquadramento do tema e as análises produzidas, foi feita uma recolha daquilo que vem sendo debatido, dando enfase ás preocupações, desafios e tendências que este tipo de empresas tem sentido. O confronto entre esses tópicos e os resultados obtidos será o grande alvo de discussão final. Importa referir que os resultados e análises produzidas foram construídas com base em dados de 464 empresas com o objetivo de produzir indicadores que permitissem obter uma análise descritiva mas também de impacto macroeconómico. Assim, foram apresentados valores referentes aos anos de 2010 até 2016, que permitissem obter uma imagem caracterizadora dos sectores de atividade, forma jurídica, localização geográfica, dimensão e performance, das empresas em estudo. A nível do impacto macroeconómico as análises visam tópicos como o Imposto sobre o Rendimento das Pessoas Coletivas, Custos com Pessoal e o Produto Interno Bruto

Nanoencapsulation of new compounds with potentioal anti-leishmanial activity

Tese de mestrado, Química Farmacêutica e Terapêutica, Universidade de Lisboa, Faculdade de Farmácia, 2016Infectious diseases caused by viruses, parasites and bacteria are currently the second cause of mortality worldwide. One of these parasites is Leishmania sp., the protozoa responsible for leishmaniasis, which is highly susceptible to oxidative stress where trypanothione reductase is an enzyme that plays a crucial role in the antioxidant defense. Endoperoxide compounds such as tetraoxanes are known to be reductively activated by iron(II)–heme to form carbon-centered radicals that will create oxidative stress in the parasite. Following this concept, new tetraoxane compounds intended as antileishmanial drugs were designed and synthesized. The synthesis reaction occurred in two steps: first the suitable ketones or aldehydes were treated with hydrogen peroxide and formic acid to give the corresponding gem-dihydroperoxide; then, trans-cynnamaldehyde and Re2O7 were added to complete conversion into 1,2,4,5-tetraoxane. Two novel tetraoxanes (23 and 24) were synthesized, characterized and loaded in solid lipid nanoparticles in order to improve the targeting capacity and effective delivery to infected macrophages. These nanosystems, composed of natural triacylglycerols are among the most promising nanostructured particulate carriers with proven in vivo efficacy in the treatment of experimental leishmaniasis, while reducing adverse side effects in non-target organs. Solid lipid nanoparticles were prepared by emulsion-solvent evaporation method, using tripalmitin as the lipid component and sodium deoxycholate, Tween® 20 and lecithin as surfactants. Particle mean diameters of solid lipid nanoparticles loaded with compounds 23 and 24 were 118 nm and 125 nm, respectively. A narrow polydispersity index and a negative surface charge were achieved for both formulations, which is suitable for their physical stability and desirable for macrophage targeting. Particle mean diameter, polydispersity index and surface charge remain unchanged after storage during 20 days at 4ºC. Encapsulation efficiencies of 87% and 88% were obtained for compounds 23 and 24, respectively. The in vitro study showed a very promising activity of formulation loaded with compound 23 against leishmania infected THP-1 cells when compared with the standard anti-leishmanial drug miltefosine. Using this strategy, new therapeutically active tetraoxanes were synthesized and loaded in solid lipid nanoparticles, demonstrating their potential as anti-leishmanial agents.As doenças infeciosas causadas por vírus, bactérias e parasitas são atualmente a segunda causa de mortalidade em todo o mundo. Um destes parasitas é a Leishmania sp., o protozoário responsável pela doença leishmaniose. Embora seja reportada como a nona doença infeciosa mais grave em todo o mundo, a leishmaniose continua a fazer parte do grupo das doenças negligenciadas, afetando sobretudo as regiões equatoriais mais pobres do globo. Segundo a Organização Mundial de Saúde, 98 países são endémicos para a leishmaniose onde estão cerca de 350 milhões de pessoas em risco de contraírem a doença, gerando aproximadamente 2 milhões de novos casos todos os anos. São conhecidas mais de 20 espécies de Leishmania infeciosas para o Homem sendo as mais comuns as seguintes: L. donovani, L. infantum, L. siamensis, L. braziliensis and L. guyanensis. A doença é transmitida através da picada de uma mosca fêmea do género phlebotomine. Existem principalmente três formas de leishmaniose: i) leishmaniose visceral, que constitui a forma mais severa da doença e que tem uma taxa de mortalidade a rondar os 100% na falta de medicação adequada; ii) leishmaniose cutânea, que representa a forma mais comum da doença; iii) leishmaniose mucocutânea que é a forma mais destrutiva da doença. Durante mais de 70 anos, a primeira linha de tratamento nos países mais afetados foram os injetáveis de antimônio pentavalente (Pentostam® and Glucantime®). No entanto, este tratamento é doloroso, potencialmente tóxico, de longa duração e tornou-se ineficaz em algumas regiões, devido ao aparecimento de resistências. A pentamidina, a paromomicina e anfotericina B fazem parte dos fármacos de segunda linha usados no tratamento da leishmaniose, mas a sua utilização é limitada devido à toxicidade e também ao aparecimento de resistências. O medicamento mais atrativo e eficaz é a formulação lipídica da anfotericina B, o Ambisome®, que apesar do índice terapêutico elevado e ausência de efeitos secundários é bastante caro e, desta forma, inacessível para os países endémicos, que são na sua maioria pobres. Finalmente, o primeiro fármaco eficaz de administração oral, a miltefosina, está associada a teratogenicidade e atividade hemolítica, e o seu tempo de semi-vida é muito longo, o que pode também originar o aparecimento de resistências. Por estas razões, e na ausência de uma vacina eficaz e barata, a necessidade de novos medicamentos eficazes contra a leishmaniose é mais urgente que nunca. A Leishmania é muito suscetível ao stress oxidativo, situação em que a enzima tripanotiona redutase desempenha um papel fundamental na defesa antioxidante. Compostos com função endoperóxido como os tetraoxanos, inicialmente desenvolvidos para o tratamento da malária, são conhecidos por serem redutivamente ativados pelo ferro (II)-heme para formarem radicais centrados no carbono e espécies reativas de oxigénio (ROS) que irão criar stress oxidativo no parasita. Seguindo este conceito, foram sintetizados novos tetraoxanos destinados a atuarem como fármacos antileishmaniose através de um mecanismo de ação duplo. Sendo assim, após a ativação do tetraoxano pelo ferro (II), por quebra da ligação peroxídica, irão formar-se dois compostos: uma primeira molécula radicalar, altamente reativa, que poderá alquilar biomoléculas essenciais para a sobrevivência do parasita, ao mesmo tempo que contribui para o aumento do stress oxidativo; forma-se também uma segunda molécula com um carbonilo α,β-insaturado na sua estrutura, que funcionará como possível inibidor da enzima tripanotiona redutase. Para obtenção dos compostos pretendidos recorreu-se a um processo de síntese que ocorreu em duas etapas: em primeiro lugar compostos carbonílicos (cetonas ou aldeídos) reagiram com peróxido de hidrogénio na presença de ácido fórmico, à temperatura ambiente, para formar o correspondente gem-dihidroperóxido; de seguida este intermediário reagiu com aldeído trans-cinâmico com catálise de Re2O7, a 0oC, para completar a conversão no 1,2,4,5-tetraoxano. Dois novos tetraoxanos (23 e 24) foram assim sintetizados, purificados e caracterizados. O rendimento final da reação de síntese do composto 23 foi de 71% e o do composto 24 foi de 47%. Ambos os compostos foram caracterizados por ressonância magnética nuclear de protão (1H-RMN), de carbono (13C-RMN) e por técnicas bidimensionais (COSY, HMQC, HMBC), espectroscopia de infravermelho, análise elementar e ponto de fusão. Os valores de análise elementar estão de acordo com os valores teóricos calculados e os pontos de fusão são 132-135oC e 96-100oC para os compostos 23 e 24, respetivamente e foram concordantes em duas técnicas diferentes (método de fusão instantâneo e DSC). Desde que foram descritas, no início dos anos 90, as nanopartículas lipídicas sólidas são vistas como uma excelente alternativa, eficaz e não tóxica, aos transportadores de fármacos coloidais mais conhecidos, como por exemplo, os lipossomas. As nanopartículas lipídicas podem ser preparadas com lípidos normalmente utilizados como excipientes farmacêuticos. As duas primeiras formas de produção destes transportadores foram a homogeneização a alta pressão e a microemulsão. A natureza coloidal e de libertação controlada permitem a proteção dos fármacos encapsulados pelas nanopartículas lipídicas sólidas, e a administração parentérica e não parentérica. Estes nano-sistemas combinam as vantagens dos lipossomas e das nanopartículas poliméricas numa só tecnologia farmacêutica. Os novos tetraoxanos foram então encapsulados em nanopartículas lipídicas sólidas, com o objetivo de melhorar a veiculação controlada e direcionada até aos macrófagos infetados. Estes nano-sistemas, constituídos por triacilgliceróis naturais estão entre os veículos mais promissores, com eficácia comprovada in vivo no tratamento da leishmaniose, reduzindo os efeitos secundários em órgãos não infetados. As nanopartículas lipídicas sólidas foram preparadas pelo método de emulsão e evaporação de solvente, utilizando a tripalmitina como componente lipídica e o desoxicolato de sódio, Tween® 20 e lecitina como tensioativos. O diâmetro médio das partículas para as formulações com os compostos 23 e 24 encapsulados foi de 118 nm e 125 nm, respetivamente. Foi obtido um índice de polidispersão baixo e potencial zeta negativo para ambas as formulações. Estes valores são adequados para a sua estabilidade física, e desejáveis para vectorização para os macrófagos. O diâmetro médio das partículas, o índice de polidispersão e o valor de potencial zeta permaneceram inalterados após o armazenamento durante 20 dias a 4ºC. Após esterilização por autoclave o diâmetro médio das partículas baixou consideravelmente, assim como a carga de fármaco. Este fenómeno foi identificado por DLS, uma vez que quando a temperatura se elevava acima do ponto de fusão da tripalmitina os tetraoxanos saíam do interior da matriz lipídica, resultando na redução do diâmetro médio das partículas. Foram obtidas eficiências de encapsulação de 87% e 88% para os compostos 23 e 24, respetivamente. Os estudos de libertação comprovaram que a formulação com o composto 23 é mais estável aos valores de pH testados (7.4 e 1.0) mas que ambas têm de ser melhoradas de forma a aumentar a gastro resistência, uma vez que estas formulações têm como fim a administração oral. Os estudos de viabilidade celular mostraram que ambos os compostos não têm toxicidade associada. O estudo in vitro em células THP-1 infetadas com L. infantum revelou atividade bastante promissora, do composto 23 encapsulado, quando comparado com a miltefosina (fármaco aprovado com atividade antileishmaniose). Esta estratégia permitiu a descoberta de novas formulações lipídicas com tetraoxanos encapsulados, como potenciais candidatos a fármacos contra a leishmaniose

PyBindE: Development of a Simple Python MM-PBSA Implementation for Estimating Protein-Protein and Protein-Ligand Binding Energies

Tese de mestrado, Bioquímica (Bioquímica), Universidade de Lisboa, Faculdade de Ciências, 2022Given the importance of proteins, the study of their interactions and binding affinities has been one of the most broadly populated fields of research for many years. Many approaches exist to calculate protein-protein and protein-ligand binding free energies, with single-trajectory MM-PBSA being a pop ular choice due to its more rigorous theoretical framework, when compared with methods, such as molec ular docking, while still possessing reasonable speed. MM-PBSA is particularly useful when the relative energy differences between system configurations are concerned, being able to provide insights about the forces involved in the binding process and their energetic contribution. In the present work, we describe a newly developed, DelPhi-based, single-trajectory MM-PBSA im plementation (PyBindE) written in Python, designed to be compatible with GROMOS force fields. A validation of this method was performed using a set of 37 HIV-1 protease-inhibitor complexes with experimentally-determined inhibition constants. These systems were also used as a validation set for g_mmpbsa, one widely used MM-PBSA implementation, originally validated using AMBER, thus com parisons with this method can be drawn. Molecular dynamics (MD) simulations of 150 ns were run in triplicate for every system, and MM-PBSA calculations were performed on the full trajectories, in 1500 snapshots per replicate. For 9 of the systems used for validation, the ligands of these systems con tained amine groups with pKa values ( 9) above physiological pH, and as such, different protonation scenarios for the ligands and the catalytic aspartate residues (Asp-25) were also explored. Furthermore, the impact of different values of the solute dielectric constant, on the correlation with experimental data, was studied for all different protonation cases. A practical application of PyBindE is also presented for the case of β-2 Microglobulin (β2M) D76N mutants, the causing-agents of a fatal form of amyloidosis. MM-PBSA was used to study the binding of 212 dimers derived from a Monte-Carlo Ensemble Docking protocol, determining the forces responsible for their binding and aggregation, and ranking the most stable binding modes. MM-PBSA calculations were run on 100 ns of MD trajectory for each dimer. Results of the comparison with g_mmpbsa are also analysed. Our validation results show an adequate correlation, 0.56, with experimental data when the correct ligand and catalytic aspartate residue protonations are employed, with a dielectric constant of 8. We found that underestimating the polar solvation contribution to the binding free energy resulted in an improvement of the correlations with our method, suggesting the need to optimize our parameterization and/or polar solvation calculation procedures. Regardless, our correlation results are higher than those reported for many standard MM-PBSA methods, with minimal parameter tweaking. The usefulness of PybindE was also highlighted in the calculation of binding free energies for β2M dimers. This method allowed the distinction of several binding modes from which different oligomerization patterns were then predicted. Overall, the results using PyBindE for the study of protein-protein binding affinities revealed a higher accuracy than g_mmpbsa, that often predicted positive binding energies suggesting unbinding events, which were not observed in the MD simulations

The Importance of the Directives Creation for the Evaluation of the Buildings Envelope Conditions in Condominium Regime Inserted in a Technical Management Model

Currently, the importance of the condominiums technical management and the organization of their annual budgets (with an enormous preponderance of the funds reserve) is not recognized in the decision-making process regarding the maintenance and rehabilitation actions of multi-family buildings. For this reason, it is of particular interest the development of models and technical tools, some that could help to list/report pathologies/anomalies as well as the correlation between the listed pathologies/anomalies and the building, and some others that may consolidate the technical legacy, in order to understand the importance of maintenance labor in the quality and durability of buildings. In this context, the article reflects, in its essence, a proposal for guidelines elaboration for the conditions assessment of the current multi-family buildings envelope in a condominium regime, carried out within the scope of a curricular unit and an ongoing PhD thesis project. The demonstration of the methodologies and tools developed for this evaluation, with the differentiation between a preliminary evaluation and a detailed evaluation, complemented by the brief presentation of a case study, is of particular importance in this work. The work also includes the reference of the importance of this study in the future development of the thesis, as well as the schematic demonstration of other complementary works for it

Realistic adversarial machine learning to improve network intrusion detection

Modern organizations can significantly benefit from the use of Artificial Intelligence (AI), and more specifically Machine Learning (ML), to tackle the growing number and increasing sophistication of cyber-attacks targeting their business processes. However, there are several technological and ethical challenges that undermine the trustworthiness of AI. One of the main challenges is the lack of robustness, which is an essential property to ensure that ML is used in a secure way. Improving robustness is no easy task because ML is inherently susceptible to adversarial examples: data samples with subtle perturbations that cause unexpected behaviors in ML models. ML engineers and security practitioners still lack the knowledge and tools to prevent such disruptions, so adversarial examples pose a major threat to ML and to the intelligent Network Intrusion Detection (NID) systems that rely on it. This thesis presents a methodology for a trustworthy adversarial robustness analysis of multiple ML models, and an intelligent method for the generation of realistic adversarial examples in complex tabular data domains like the NID domain: Adaptative Perturbation Pattern Method (A2PM). It is demonstrated that a successful adversarial attack is not guaranteed to be a successful cyber-attack, and that adversarial data perturbations can only be realistic if they are simultaneously valid and coherent, complying with the domain constraints of a real communication network and the class-specific constraints of a certain cyber-attack class. A2PM can be used for adversarial attacks, to iteratively cause misclassifications, and adversarial training, to perform data augmentation with slightly perturbed data samples. Two case studies were conducted to evaluate its suitability for the NID domain. The first verified that the generated perturbations preserved both validity and coherence in Enterprise and Internet-of Things (IoT) network scenarios, achieving realism. The second verified that adversarial training with simple perturbations enables the models to retain a good generalization to regular IoT network traffic flows, in addition to being more robust to adversarial examples. The key takeaway of this thesis is: ML models can be incredibly valuable to improve a cybersecurity system, but their own vulnerabilities must not be disregarded. It is essential to continue the research efforts to improve the security and trustworthiness of ML and of the intelligent systems that rely on it.Organizações modernas podem beneficiar significativamente do uso de Inteligência Artificial (AI), e mais especificamente Aprendizagem Automática (ML), para enfrentar a crescente quantidade e sofisticação de ciberataques direcionados aos seus processos de negócio. No entanto, há vários desafios tecnológicos e éticos que comprometem a confiabilidade da AI. Um dos maiores desafios é a falta de robustez, que é uma propriedade essencial para garantir que se usa ML de forma segura. Melhorar a robustez não é uma tarefa fácil porque ML é inerentemente suscetível a exemplos adversos: amostras de dados com perturbações subtis que causam comportamentos inesperados em modelos ML. Engenheiros de ML e profissionais de segurança ainda não têm o conhecimento nem asferramentas necessárias para prevenir tais disrupções, por isso os exemplos adversos representam uma grande ameaça a ML e aos sistemas de Deteção de Intrusões de Rede (NID) que dependem de ML. Esta tese apresenta uma metodologia para uma análise da robustez de múltiplos modelos ML, e um método inteligente para a geração de exemplos adversos realistas em domínios de dados tabulares complexos como o domínio NID: Método de Perturbação com Padrões Adaptativos (A2PM). É demonstrado que um ataque adverso bem-sucedido não é garantidamente um ciberataque bem-sucedido, e que as perturbações adversas só são realistas se forem simultaneamente válidas e coerentes, cumprindo as restrições de domínio de uma rede de computadores real e as restrições específicas de uma certa classe de ciberataque. A2PM pode ser usado para ataques adversos, para iterativamente causar erros de classificação, e para treino adverso, para realizar aumento de dados com amostras ligeiramente perturbadas. Foram efetuados dois casos de estudo para avaliar a sua adequação ao domínio NID. O primeiro verificou que as perturbações preservaram tanto a validade como a coerência em cenários de redes Empresariais e Internet-das-Coisas (IoT), alcançando o realismo. O segundo verificou que o treino adverso com perturbações simples permitiu aos modelos reter uma boa generalização a fluxos de tráfego de rede IoT, para além de serem mais robustos contra exemplos adversos. A principal conclusão desta tese é: os modelos ML podem ser incrivelmente valiosos para melhorar um sistema de cibersegurança, mas as suas próprias vulnerabilidades não devem ser negligenciadas. É essencial continuar os esforços de investigação para melhorar a segurança e a confiabilidade de ML e dos sistemas inteligentes que dependem de ML

IoTMapper: a metrics aggregation system architecture in support of smart city solutions

Smart cities are, nowadays, an unavoidable and growing reality, supported on software platforms that support city management, through the processing and presentation of a large number of data, obtained from sensors used throughout the cities. Low-power wide area networks (LPWAN) leverage the sensorization process; however, urban landscape, in turn, induces a high probability of change in the propagation conditions of the LPWAN network, thus requiring active monitoring solutions for assessing the city LPWAN network condition. Currently existing solutions usually consider the existence of only one type of LPWAN network to be monitored. In this paper, an architecture for aggregation of metrics from heterogeneous LPWAN networks is presented. The architecture, named IoTMapper, combines purpose build components with existing components from the FIWARE and Apache Kafka ecosystems. Implementation details for the LPWAN networks are abstracted by adapters so that new networks may be easily added. The validation was carried out using real data collected for long-range wide-area network (LoRaWAN) in Lisbon, and a simulated data set extrapolated from the collected data. The results indicate that the presented architecture is a viable solution for metrics aggregation that may be expanded to support multiple networks. However, some of the considered FIWARE components present performance bottlenecks that may hinder the scaling of the architecture while processing new message arrivals.info:eu-repo/semantics/publishedVersio

Adaptative Perturbation Patterns: Realistic Adversarial Learning for Robust Intrusion Detection

Adversarial attacks pose a major threat to machine learning and to the systems that rely on it. In the cybersecurity domain, adversarial cyber-attack examples capable of evading detection are especially concerning. Nonetheless, an example generated for a domain with tabular data must be realistic within that domain. This work establishes the fundamental constraint levels required to achieve realism and introduces the Adaptative Perturbation Pattern Method (A2PM) to fulfill these constraints in a gray-box setting. A2PM relies on pattern sequences that are independently adapted to the characteristics of each class to create valid and coherent data perturbations. The proposed method was evaluated in a cybersecurity case study with two scenarios: Enterprise and Internet of Things (IoT) networks. Multilayer Perceptron (MLP) and Random Forest (RF) classifiers were created with regular and adversarial training, using the CIC-IDS2017 and IoT-23 datasets. In each scenario, targeted and untargeted attacks were performed against the classifiers, and the generated examples were compared with the original network traffic flows to assess their realism. The obtained results demonstrate that A2PM provides a scalable generation of realistic adversarial examples, which can be advantageous for both adversarial training and attacks.Comment: 18 pages, 6 tables, 10 figures, Future Internet journa

SoK: Realistic Adversarial Attacks and Defenses for Intelligent Network Intrusion Detection

Machine Learning (ML) can be incredibly valuable to automate anomaly detection and cyber-attack classification, improving the way that Network Intrusion Detection (NID) is performed. However, despite the benefits of ML models, they are highly susceptible to adversarial cyber-attack examples specifically crafted to exploit them. A wide range of adversarial attacks have been created and researchers have worked on various defense strategies to safeguard ML models, but most were not intended for the specific constraints of a communication network and its communication protocols, so they may lead to unrealistic examples in the NID domain. This Systematization of Knowledge (SoK) consolidates and summarizes the state-of-the-art adversarial learning approaches that can generate realistic examples and could be used in real ML development and deployment scenarios with real network traffic flows. This SoK also describes the open challenges regarding the use of adversarial ML in the NID domain, defines the fundamental properties that are required for an adversarial example to be realistic, and provides guidelines for researchers to ensure that their future experiments are adequate for a real communication network.Comment: 31 pages, 3 tables, 6 figures, Computers and Security journa

Reasons for dropout in swimmers, differences between gender and age and intentions to return to competition

BACKGROUND: This study’s main purpose was to analyze reasons for dropout in competitive swimmers and differences between gender and age groups. The influence of dropout on swimmers intentions to return to competition, invariance across gender and validation of Questionnaire of Reasons for Attrition were also analyzed. METHODS: Study 1 – 366 athletes participated (N.=366; mean age 15.96, SD 5.99) and the data gathered was used for the exploratory analysis, and data gathered on 1008 athletes were used for the confirmatory analysis and the structural equations (N.=1008; mean age 16.26, SD 6.12); Study 2: 1008 athletes participated (N.=1008; mean age 16.26, SD 6.12) on the descriptive and inferential analysis of the reasons behind the practice dropout. The Questionnaire of Reasons Attrition was used in both studies to assess the reasons associated with the practice dropout. RESULTS: In study 1, the results showed an acceptable fit of the measurement model and invariance across gender and also predictive validity regarding swimmers intentions to return to competition (e.g., demands/pressure” negatively predict intentions). In study 2, the main results showed that the most significant reason for dropout in both genders and all age groups was “dissatisfaction/other priorities”; the study also showed there to be differences between gender and age groups (e.g., female and younger athletes valued “demands/ pressure “more). CONCLUSIONS: This study offers useful guidelines for the training process and to support decisions on sports politics to be implemented to overcome the dropout rate. However, it is important to broaden the evidence to other sports and implement programs on identified priority areas based on longitudinal perspectives.info:eu-repo/semantics/publishedVersio

SoK: Realistic Adversarial Attacks and Defenses for Intelligent Network Intrusion Detection

Machine Learning (ML) can be incredibly valuable to automate anomaly detection and cyber-attack classification, improving the way that Network Intrusion Detection (NID) is performed. However, despite the benefits of ML models, they are highly susceptible to adversarial cyber-attack examples specifically crafted to exploit them. A wide range of adversarial attacks have been created and researchers have worked on various defense strategies to safeguard ML models, but most were not intended for the specific constraints of a communication network and its communication protocols, so they may lead to unrealistic examples in the NID domain. This Systematization of Knowledge (SoK) consolidates and summarizes the state-of-the-art adversarial learning approaches that can generate realistic examples and could be used in real ML development and deployment scenarios with real network traffic flows. This SoK also describes the open challenges regarding the use of adversarial ML in the NID domain, defines the fundamental properties that are required for an adversarial example to be realistic, and provides guidelines for researchers to ensure that their future experiments are adequate for a real communication network.The present work was partially supported by the Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF), within project ”Cybers SeC IP” (NORTE-01-0145-FEDER000044). This work has also received funding from UIDB/00760/2020.info:eu-repo/semantics/acceptedVersio