11,570 research outputs found
Endogenous measures for contextualising large-scale social phenomena: a corpus-based method for mediated public discourse
This work presents an interdisciplinary methodology for developing endogenous measures of group membership through analysis of pervasive linguistic patterns in public discourse. Focusing on political discourse, this work critiques the conventional approach to the study of political participation, which is premised on decontextualised, exogenous measures to characterise groups. Considering the theoretical and empirical weaknesses of decontextualised approaches to large-scale social phenomena, this work suggests that contextualisation using endogenous measures might provide a complementary perspective to mitigate such weaknesses.
This work develops a sociomaterial perspective on political participation in mediated discourse as affiliatory action performed through language. While the affiliatory function of language is often performed consciously (such as statements of identity), this work is concerned with unconscious features (such as patterns in lexis and grammar). This work argues that pervasive patterns in such features that emerge through socialisation are resistant to change and manipulation, and thus might serve as endogenous measures of sociopolitical contexts, and thus of groups.
In terms of method, the work takes a corpus-based approach to the analysis of data from the Twitter messaging service whereby patterns in users’ speech are examined statistically in order to trace potential community membership. The method is applied in the US state of Michigan during the second half of 2018—6 November having been the date of midterm (i.e. non-Presidential) elections in the United States. The corpus is assembled from the original posts of 5,889 users, who are nominally geolocalised to 417 municipalities. These users are clustered according to pervasive language features. Comparing the linguistic clusters according to the municipalities they represent finds that there are regular sociodemographic differentials across clusters. This is understood as an indication of social structure, suggesting that endogenous measures derived from pervasive patterns in language may indeed offer a complementary, contextualised perspective on large-scale social phenomena
The Metaverse: Survey, Trends, Novel Pipeline Ecosystem & Future Directions
The Metaverse offers a second world beyond reality, where boundaries are
non-existent, and possibilities are endless through engagement and immersive
experiences using the virtual reality (VR) technology. Many disciplines can
benefit from the advancement of the Metaverse when accurately developed,
including the fields of technology, gaming, education, art, and culture.
Nevertheless, developing the Metaverse environment to its full potential is an
ambiguous task that needs proper guidance and directions. Existing surveys on
the Metaverse focus only on a specific aspect and discipline of the Metaverse
and lack a holistic view of the entire process. To this end, a more holistic,
multi-disciplinary, in-depth, and academic and industry-oriented review is
required to provide a thorough study of the Metaverse development pipeline. To
address these issues, we present in this survey a novel multi-layered pipeline
ecosystem composed of (1) the Metaverse computing, networking, communications
and hardware infrastructure, (2) environment digitization, and (3) user
interactions. For every layer, we discuss the components that detail the steps
of its development. Also, for each of these components, we examine the impact
of a set of enabling technologies and empowering domains (e.g., Artificial
Intelligence, Security & Privacy, Blockchain, Business, Ethics, and Social) on
its advancement. In addition, we explain the importance of these technologies
to support decentralization, interoperability, user experiences, interactions,
and monetization. Our presented study highlights the existing challenges for
each component, followed by research directions and potential solutions. To the
best of our knowledge, this survey is the most comprehensive and allows users,
scholars, and entrepreneurs to get an in-depth understanding of the Metaverse
ecosystem to find their opportunities and potentials for contribution
A Design Science Research Approach to Smart and Collaborative Urban Supply Networks
Urban supply networks are facing increasing demands and challenges and thus constitute a relevant field for research and practical development. Supply chain management holds enormous potential and relevance for society and everyday life as the flow of goods and information are important economic functions. Being a heterogeneous field, the literature base of supply chain management research is difficult to manage and navigate. Disruptive digital technologies and the implementation of cross-network information analysis and sharing drive the need for new organisational and technological approaches. Practical issues are manifold and include mega trends such as digital transformation, urbanisation, and environmental awareness.
A promising approach to solving these problems is the realisation of smart and collaborative supply networks. The growth of artificial intelligence applications in recent years has led to a wide range of applications in a variety of domains. However, the potential of artificial intelligence utilisation in supply chain management has not yet been fully exploited. Similarly, value creation increasingly takes place in networked value creation cycles that have become continuously more collaborative, complex, and dynamic as interactions in business processes involving information technologies have become more intense.
Following a design science research approach this cumulative thesis comprises the development and discussion of four artefacts for the analysis and advancement of smart and collaborative urban supply networks. This thesis aims to highlight the potential of artificial intelligence-based supply networks, to advance data-driven inter-organisational collaboration, and to improve last mile supply network sustainability. Based on thorough machine learning and systematic literature reviews, reference and system dynamics modelling, simulation, and qualitative empirical research, the artefacts provide a valuable contribution to research and practice
Anuário científico da Escola Superior de Tecnologia da Saúde de Lisboa - 2021
É com grande prazer que apresentamos a mais recente edição (a 11.ª) do Anuário Científico da Escola Superior de Tecnologia da Saúde de Lisboa. Como instituição de ensino superior, temos o compromisso de promover e incentivar a pesquisa científica em todas as áreas do conhecimento que contemplam a nossa missão. Esta publicação tem como objetivo divulgar toda a produção científica desenvolvida pelos Professores, Investigadores, Estudantes e Pessoal não Docente da ESTeSL durante 2021. Este Anuário é, assim, o reflexo do trabalho árduo e dedicado da nossa comunidade, que se empenhou na produção de conteúdo científico de elevada qualidade e partilhada com a Sociedade na forma de livros, capítulos de livros, artigos publicados em revistas nacionais e internacionais, resumos de comunicações orais e pósteres, bem como resultado dos trabalhos de 1º e 2º ciclo. Com isto, o conteúdo desta publicação abrange uma ampla variedade de tópicos, desde temas mais fundamentais até estudos de aplicação prática em contextos específicos de Saúde, refletindo desta forma a pluralidade e diversidade de áreas que definem, e tornam única, a ESTeSL. Acreditamos que a investigação e pesquisa científica é um eixo fundamental para o desenvolvimento da sociedade e é por isso que incentivamos os nossos estudantes a envolverem-se em atividades de pesquisa e prática baseada na evidência desde o início dos seus estudos na ESTeSL. Esta publicação é um exemplo do sucesso desses esforços, sendo a maior de sempre, o que faz com que estejamos muito orgulhosos em partilhar os resultados e descobertas dos nossos investigadores com a comunidade científica e o público em geral. Esperamos que este Anuário inspire e motive outros estudantes, profissionais de saúde, professores e outros colaboradores a continuarem a explorar novas ideias e contribuir para o avanço da ciência e da tecnologia no corpo de conhecimento próprio das áreas que compõe a ESTeSL. Agradecemos a todos os envolvidos na produção deste anuário e desejamos uma leitura inspiradora e agradável.info:eu-repo/semantics/publishedVersio
TOWARDS AN UNDERSTANDING OF EFFORTFUL FUNDRAISING EXPERIENCES: USING INTERPRETATIVE PHENOMENOLOGICAL ANALYSIS IN FUNDRAISING RESEARCH
Physical-activity oriented community fundraising has experienced an exponential growth in popularity over the past 15 years. The aim of this study was to explore the value of effortful fundraising experiences, from the point of view of participants, and explore the impact that these experiences have on people’s lives. This study used an IPA approach to interview 23 individuals, recognising the role of participants as proxy (nonprofessional) fundraisers for charitable organisations, and the unique organisation donor dynamic that this creates. It also bought together relevant psychological theory related to physical activity fundraising experiences (through a narrative literature review) and used primary interview data to substantiate these. Effortful fundraising experiences are examined in detail to understand their significance to participants, and how such experiences influence their connection with a charity or cause. This was done with an idiographic focus at first, before examining convergences and divergences across the sample. This study found that effortful fundraising experiences can have a profound positive impact upon community fundraisers in both the short and the long term. Additionally, it found that these experiences can be opportunities for charitable organisations to create lasting meaningful relationships with participants, and foster mutually beneficial lifetime relationships with them. Further research is needed to test specific psychological theory in this context, including self-esteem theory, self determination theory, and the martyrdom effect (among others)
Predictive Maintenance of Critical Equipment for Floating Liquefied Natural Gas Liquefaction Process
Predictive Maintenance of Critical Equipment for Liquefied Natural Gas Liquefaction Process
Meeting global energy demand is a massive challenge, especially with the quest of more affinity towards sustainable and cleaner energy. Natural gas is viewed as a bridge fuel to a renewable energy. LNG as a processed form of natural gas is the fastest growing and cleanest form of fossil fuel. Recently, the unprecedented increased in LNG demand, pushes its exploration and processing into offshore as Floating LNG (FLNG). The offshore topsides gas processes and liquefaction has been identified as one of the great challenges of FLNG. Maintaining topside liquefaction process asset such as gas turbine is critical to profitability and reliability, availability of the process facilities. With the setbacks of widely used reactive and preventive time-based maintenances approaches, to meet the optimal reliability and availability requirements of oil and gas operators, this thesis presents a framework driven by AI-based learning approaches for predictive maintenance. The framework is aimed at leveraging the value of condition-based maintenance to minimises the failures and downtimes of critical FLNG equipment (Aeroderivative gas turbine).
In this study, gas turbine thermodynamics were introduced, as well as some factors affecting gas turbine modelling. Some important considerations whilst modelling gas turbine system such as modelling objectives, modelling methods, as well as approaches in modelling gas turbines were investigated. These give basis and mathematical background to develop a gas turbine simulated model. The behaviour of simple cycle HDGT was simulated using thermodynamic laws and operational data based on Rowen model. Simulink model is created using experimental data based on Rowen’s model, which is aimed at exploring transient behaviour of an industrial gas turbine. The results show the capability of Simulink model in capture nonlinear dynamics of the gas turbine system, although constraint to be applied for further condition monitoring studies, due to lack of some suitable relevant correlated features required by the model.
AI-based models were found to perform well in predicting gas turbines failures. These capabilities were investigated by this thesis and validated using an experimental data obtained from gas turbine engine facility. The dynamic behaviours gas turbines changes when exposed to different varieties of fuel. A diagnostics-based AI models were developed to diagnose different gas turbine engine’s failures associated with exposure to various types of fuels. The capabilities of Principal Component Analysis (PCA) technique have been harnessed to reduce the dimensionality of the dataset and extract good features for the diagnostics model development.
Signal processing-based (time-domain, frequency domain, time-frequency domain) techniques have also been used as feature extraction tools, and significantly added more correlations to the dataset and influences the prediction results obtained. Signal processing played a vital role in extracting good features for the diagnostic models when compared PCA. The overall results obtained from both PCA, and signal processing-based models demonstrated the capabilities of neural network-based models in predicting gas turbine’s failures. Further, deep learning-based LSTM model have been developed, which extract features from the time series dataset directly, and hence does not require any feature extraction tool. The LSTM model achieved the highest performance and prediction accuracy, compared to both PCA-based and signal processing-based the models.
In summary, it is concluded from this thesis that despite some challenges related to gas turbines Simulink Model for not being integrated fully for gas turbine condition monitoring studies, yet data-driven models have proven strong potentials and excellent performances on gas turbine’s CBM diagnostics. The models developed in this thesis can be used for design and manufacturing purposes on gas turbines applied to FLNG, especially on condition monitoring and fault detection of gas turbines. The result obtained would provide valuable understanding and helpful guidance for researchers and practitioners to implement robust predictive maintenance models that will enhance the reliability and availability of FLNG critical equipment.Petroleum Technology Development Funds (PTDF) Nigeri
Defining Service Level Agreements in Serverless Computing
The emergence of serverless computing has brought significant advancements to the delivery of computing resources to cloud users. With the abstraction of infrastructure, ecosystem, and execution environments, users could focus on their code while relying on the cloud provider to manage the abstracted layers. In addition, desirable features such as autoscaling and high availability became a provider’s responsibility and can be adopted by the user\u27s application at no extra overhead.
Despite such advancements, significant challenges must be overcome as applications transition from monolithic stand-alone deployments to the ephemeral and stateless microservice model of serverless computing. These challenges pertain to the uniqueness of the conceptual and implementation models of serverless computing. One of the notable challenges is the complexity of defining Service Level Agreements (SLA) for serverless functions. As the serverless model shifts the administration of resources, ecosystem, and execution layers to the provider, users become mere consumers of the provider’s abstracted platform with no insight into its performance. Suboptimal conditions of the abstracted layers are not visible to the end-user who has no means to assess their performance. Thus, SLA in serverless computing must take into consideration the unique abstraction of its model.
This work investigates the Service Level Agreement (SLA) modeling of serverless functions\u27 and serverless chains’ executions. We highlight how serverless SLA fundamentally differs from earlier cloud delivery models. We then propose an approach to define SLA for serverless functions by utilizing resource utilization fingerprints for functions\u27 executions and a method to assess if executions adhere to that SLA. We evaluate the approach’s accuracy in detecting SLA violations for a broad range of serverless application categories. Our validation results illustrate a high accuracy in detecting SLA violations resulting from resource contentions and provider’s ecosystem degradations. We conclude by presenting the empirical validation of our proposed approach, which could detect Execution-SLA violations with accuracy up to 99%
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After Creation: Intergovernmental Organizations and Member State Governments as Co-Participants in an Authority Relationship
This is a re-amalgamation of what started as one manuscript and became two when the length proved to be more than any publisher wanted to consider. The splitting consisted of removing what are now Parts 3, 4, and 5 so that the manuscript focused on the outcome-related shared beliefs holding an authority relationship together. Those parts were last worked on in 2018. The rest were last worked on in late 2021 but also remain incomplete.
The relational approach adopted in this study treats intergovernmental organizations and the governments of member states as co-participants in an authority relationship with the governments of their member states. Authority relationships link two types of actor, defined by their authority-holder or addressee role in the relationship, through a set of shared beliefs about why the relationship exists and how the participants should fulfill their respective roles. The IGO as authority holder has a role that includes a right to instruct other actors about what they should or should not do; the governments of member states as addressees are expected to comply with the instructions. Three sets of shared beliefs provide the conceptual “glue” holding the relationship together. The first defines the goal of the collective effort, providing both the rationale for having the authority relationship and providing a lode star for assessments of the collective effort’s success or lack of success. The second set defines the shared understanding about allocation of roles and the process of interaction by establishing shared expectations about a) the selection process by which particular actors acquire authority holder roles, b) the definitions identifying one or more categories of addressees expected to follow instructions, and c) the procedures through which the authority holder issues instructions. The third set focus on the outcomes of cooperation through the relationship by defining a) the substantive areas in which the authority holder may issue instructions, b) the bases for assessing the relevance actions mandated in instructions for reaching the goal, and c) the relative efficacy of action paths chosen for reaching the goal as compared to other possible action paths.
Using an authority relationship framework for analyzing cooperation through IGOs highlights the inherently bi-directional nature of IGO-member government activity by viewing their interaction as involving a three-step process in which the IGO as authority holder decides when to issue what instruction, the member state governments as followers react to the instruction with anything from prompt and full compliance through various forms of pushback to outright rejection, and the IGO as authority holder responds to how the followers react with efforts to increase individual compliance with instructions and reinforce continuing acceptance of the authority relationship. Foregrounding the dynamics produced by the interaction of these two streams of perception and action reveals more clearly how far intergovernmental organizations acquire capacity to operate as independent actors, the dynamic ways they maintain that capacity, and how much they influence member governments’ beliefs and actions at different times. The approach fosters better understanding of why, when, and for how long governments choose cooperation through an IGO even in periods of rising unilateralism
Biomarcadores salivares no cancro da cabeça e pescoço
Head and neck cancer (HNC) are a group of cancers which occur in the organs from the anatomical area referred. The HNC staging system is generalized for most subtypes of HNC and restricted for the seven classifying stages of this cancer, which affects the diagnosis and, therefore, the treatment. The possible biomarkers identified for HNC are either non-specific or still waiting for clinical validations. Exosomes are nanovesicles which reflect the molecular composition of the cell of origin, including tumour cells, and can be found in biofluids as saliva, a proximal and easily accessible fluid for HNC. As exosome’ purification methods are being developed, the study of the proteome of salivary exosomes of HNC patients is a promising approach for identification of specific biomarkers for HNC and early diagnosis and prognosis of the cancer.
In this thesis project, unstimulated saliva was collected from fifteen HNC patients and three healthy subjects and exosomes were extracted. Protein quantification was performed, and the proteins were analysed by LC-MS, after protein separation with SDS-PAGE and in-gel trypsin digestion.
The proteomes of salivary exosomes of HNC demonstrated to be involved in a panoply of biological processes mainly focused in exocytosis, inflammatory response and tumour-related processes, which were expected in this study. The overexpression of α-amylase and immunoglobulin A in salivary exosomes of HNC, when compared with the healthy controls, is considered as a potential HNC biomarker. From the several proteins exclusively in either sample 1, 2, 8, 10, 14 and 15, suprabasin, protein S100-A7, S100-P and cathepsin B were already described as HNC possible biomarker in another type of samples.
This study demonstrated that the salivary exosomes are capable to reflect the pathophysiological status of HNC. Furthermore, overexpression of α-amylase and immunoglobulin A in salivary exosomes may be a non-specific biomarker of HNC and the previously described HNC biomarkers need to be further studied in order to understand in which conditions they are expressed.O cancro da cabeça e pescoço (CCP) corresponde a um grupo de cancros que ocorrem nos órgãos da área anatómica referida. O sistema de classificação do CCP é generalizado para a maioria dos subtipos de CCP e restrito aos sete estágios de classificação desse cancro, o que afeta o diagnóstico e, portanto, o tratamento. Os possíveis biomarcadores identificados para CCP ou não são específicos, ou ainda aguardam validações clínicas. Os exossomas são nanovesículas que refletem a composição molecular da sua célula de origem, inclusive células tumorais, e podem ser encontrados em bio fluídos como saliva, um fluído proximal e de fácil acesso. À medida que os métodos de purificação dos exossomas estão a ser desenvolvidos, o estudo do proteoma de exossomas salivares de pacientes com CCP surge como uma abordagem promissora para a identificação de biomarcadores específicos para o mesmo, e diagnóstico e prognóstico precoces do cancro.
Neste projeto de tese, a saliva não estimulada foi recolhida de quinze pacientes e três sujeitos saudáveis com CCP e os exossomas foram extraídos. A quantificação de proteínas foi realizada e as proteínas foram analisadas por LC-MS, após separação das proteínas com SDS-PAGE e digestão com tripsina em gel.
Os proteomas dos exossomas salivares do HNC demonstraram estar envolvidos numa panóplia de processos biológicos direcionados principalmente para exocitose, resposta inflamatória e processos relacionados a tumores, algo esperado neste estudo. A sobre expressão de α-amilase e imunoglobulina A nos exossomas salivares de HNC, quando comparada com os controlos saudáveis, é considerada um potencial biomarcador de CCP. Das várias proteínas exclusivas nas amostras 1, 2, 8, 10, 14 e 15, a suprabasina, a proteína S100-A7, S100-P e a catepsina B já foram descritas como possíveis biomarcadores do HNC noutro tipo de amostras.
Assim, este estudo demonstrou que os exossomas salivares são capazes de refletir o estatuto fisiopatológico do HNC e que a sobre expressão de α-amilase e imunoglobulina A em exossomas salivares pode ser um biomarcador não específico de HNC. Relativamente aos restantes biomarcadores de HNC descritos serão necessários mais estudos para entender em que condições são expressos.Mestrado em Bioquímic
AIUCD 2022 - Proceedings
L’undicesima edizione del Convegno Nazionale dell’AIUCD-Associazione di Informatica Umanistica ha per titolo Culture digitali. Intersezioni: filosofia, arti, media. Nel titolo è presente, in maniera esplicita, la richiesta di una riflessione, metodologica e teorica, sull’interrelazione tra tecnologie digitali, scienze dell’informazione, discipline filosofiche, mondo delle arti e cultural studies
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