12,798 research outputs found

    Revisão taxonómica do género Calendula L. (Asteraceae - Calenduleae) na Península Ibérica e Marrocos

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    The genus Calendula L. (Asteraceae - Calenduleae) includes, depending on the author, 10 to 25 species, distributed mainly in the Mediterranean basin. The taxonomy of this genus is considered to be extremely difficult, due to a great morphological variability, doubtfull relevance of some of the characters used to distinguish its species (e.g. the life form: annual or perennial; the habit: erect or diffuse, shape of the leaves, indumentum, relative size of the capitula and colour of disc or ray florets, achene morphology), but also due to the hybridization and polyploidization. Despite the numerous studies that have been published, no agreement on the classification and characters used to discriminate between taxa has been reached. A taxonomic study of the genus Calendula was conducted for the Iberian Peninsula and Morocco, aiming at (1) access the morphological variability between and within taxa, (2) confirm the chromosome numbers, (3) increase the nuclear DNA content estimations, (4) re-evaluate taxa delimitations and circumscription, and (5) reassess, and redefine, the descriptions and characters useful to distinguish taxa. In order to achieve a satisfying taxonomic core, extensive fieldwork, detailed morphometric analysis, chorological, karyological and genome size studies were conducted. For the Iberian Peninsula, four species were recognized, including nine subspecies (between these two new subspecies were described). For Morocco, including some taxa from Algeria and Tunisia 13 species were recognized (two new species and a nomenclatural change), including 15 subspecies (among these eight new subspecies were described). To corroborate the results obtained and to evaluate the evolutionary relationships among taxa, phylogenetic studies using molecular methods, such as ITS, microsatellites or other molecular markers, should be used.O gĂ©nero Calendula L. (Asteraceae - Calenduleae) inclui, dependendo do autor, 10 a 25 espĂ©cies, distribuĂ­das essencialmente na bacia do MediterrĂąneo. A taxonomia deste gĂ©nero Ă© considerada extremamente difĂ­cil, devido Ă  grande variabilidade morfolĂłgica, discutivel relevĂąncia de alguns dos caracteres utilizados para distinguir suas espĂ©cies (por exemplo, a forma de vida: anual ou perene, o hĂĄbito: erecto ou difuso, a forma das folhas, o indumento, o tamanho e a cor dos capĂ­tulos e a morfologia dos aquĂ©nios), mas tambĂ©m devido Ă  hibridização e poliploidização. Apesar dos inĂșmeros estudos que foram publicados, nĂŁo foi alcançado um acordo sobre a classificação e os caracteres utilizados para discriminar as suas espĂ©cies. Um estudo taxonĂłmico do gĂ©nero Calendula foi realizado para a PenĂ­nsula IbĂ©rica e Marrocos, com o objectivo de (1) verificar a variabilidade morfolĂłgica, (2) confirmar o nĂșmero de cromossomas, (3) aumentar as estimativas de conteĂșdo em ADN, (4) reavaliar a delimitação e a circunscrição dos taxa, e (5) reavaliar e redefinir as descriçÔes e caracteres Ășteis para os distinguir. Para alcançar uma robustĂȘs taxonĂłmica satisfatĂłria, foram realizados extensos trabalhos de campo, anĂĄlise morfomĂ©trica detalhada, abordagens corolĂłgicas, cariolĂłgicas e quanto ao conteĂșdo em ADN. Para a PenĂ­nsula IbĂ©rica, quatro espĂ©cies foram reconhecidas, incluindo nove subespĂ©cies (entre essas duas novas subespĂ©cies foram descritas). Para Marrocos, incluindo alguns taxa da Argelia e Tunisia, foram reconhecidas 13 espĂ©cies (duas novas e uma mudança nomenclatural), incluindo 15 subespĂ©cies (entre essas oito novas subespĂ©cies foram descritas). Para corroborar os resultados obtidos e avaliar as relaçÔes evolutivas e filogenĂ©ticas entre os taxa, estudos que utilizem diferentes mĂ©todos moleculares, tais como ITS, microsatĂ©lites ou outros marcadores moleculares, devem ser utilizados.Apoio financeiro do LaboratĂłrio Associado CESAM - Centro de Estudos do Ambiente e do Mar (AMB/50017) financiado por fundos nacionais atravĂ©s da FCT/MCTES e cofinanciado pelo FEDER (POCI-01-0145-FEDER-007638), no Ăąmbito do Acordo de Parceria PT2020, e Compete 2020Programa Doutoral em Biologi

    The Metaverse: Survey, Trends, Novel Pipeline Ecosystem & Future Directions

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    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

    What do new performance metrics, VeDBA and Dynamic yaw, tell us about energy-intensive activities in whale sharks?

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    During oscillatory dives, whale sharks (Rhincodon typus) expend varying levels of energy in active ascent and passive descent. They are expected to minimise movement costs by travelling at optimum speed unless having reason to move faster, for example during feeding or evasion of danger. A proxy for power, dynamic body acceleration (DBA) has previously been used to identify whale shark movement patterns but has yet been used to identify occasions where power is elevated above minimum requirements. 59 hours of biologging data from 13 juvenile whale sharks (Ningaloo Reef, Western Australia) including depth, body pitch angle, magnetometry and DBA, was analysed to investigate minimum power requirements for dives and identify events of elevated power. Dynamic yaw (the rate of change of heading), a new proxy for power, was introduced to determine its effectiveness compared to the already-established DBA. The relationship between pitch angle and these two proxies was investigated to determine which had the stronger relationship. Dynamic yaw produced a poor relationship with pitch angle compared to DBA, and thus DBA was selected as the focus proxy for the remainder of the study. DBA was utilised to produce a minimum power trend versus body pitch angle using a convex hull analysis which allowed for the identification of proxy for power utilisation above the minimum (PAM). 16 instances of PAM were identified in 59 hours of data, which could all be considered instances where energy minimisation is not prioritised, such as feeding or avoidance. The PAM method was capable of identifying instances where energy minimisation is not prioritised, and therefore has future implications in investigations of location-specific behaviours in relation to feeding and anthropogenic disturbance

    Countermeasures for the majority attack in blockchain distributed systems

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    La tecnologĂ­a Blockchain es considerada como uno de los paradigmas informĂĄticos mĂĄs importantes posterior al Internet; en funciĂłn a sus caracterĂ­sticas Ășnicas que la hacen ideal para registrar, verificar y administrar informaciĂłn de diferentes transacciones. A pesar de esto, Blockchain se enfrenta a diferentes problemas de seguridad, siendo el ataque del 51% o ataque mayoritario uno de los mĂĄs importantes. Este consiste en que uno o mĂĄs mineros tomen el control de al menos el 51% del Hash extraĂ­do o del cĂłmputo en una red; de modo que un minero puede manipular y modificar arbitrariamente la informaciĂłn registrada en esta tecnologĂ­a. Este trabajo se enfocĂł en diseñar e implementar estrategias de detecciĂłn y mitigaciĂłn de ataques mayoritarios (51% de ataque) en un sistema distribuido Blockchain, a partir de la caracterizaciĂłn del comportamiento de los mineros. Para lograr esto, se analizĂł y evaluĂł el Hash Rate / Share de los mineros de Bitcoin y Crypto Ethereum, seguido del diseño e implementaciĂłn de un protocolo de consenso para controlar el poder de cĂłmputo de los mineros. Posteriormente, se realizĂł la exploraciĂłn y evaluaciĂłn de modelos de Machine Learning para detectar software malicioso de tipo Cryptojacking.DoctoradoDoctor en IngenierĂ­a de Sistemas y ComputaciĂł

    Associated Random Neural Networks for Collective Classification of Nodes in Botnet Attacks

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    Botnet attacks are a major threat to networked systems because of their ability to turn the network nodes that they compromise into additional attackers, leading to the spread of high volume attacks over long periods. The detection of such Botnets is complicated by the fact that multiple network IP addresses will be simultaneously compromised, so that Collective Classification of compromised nodes, in addition to the already available traditional methods that focus on individual nodes, can be useful. Thus this work introduces a collective Botnet attack classification technique that operates on traffic from an n-node IP network with a novel Associated Random Neural Network (ARNN) that identifies the nodes which are compromised. The ARNN is a recurrent architecture that incorporates two mutually associated, interconnected and architecturally identical n-neuron random neural networks, that act simultneously as mutual critics to reach the decision regarding which of n nodes have been compromised. A novel gradient learning descent algorithm is presented for the ARNN, and is shown to operate effectively both with conventional off-line training from prior data, and with on-line incremental training without prior off-line learning. Real data from a 107 node packet network is used with over 700,000 packets to evaluate the ARNN, showing that it provides accurate predictions. Comparisons with other well-known state of the art methods using the same learning and testing datasets, show that the ARNN offers significantly better performance

    Machine Learning Research Trends in Africa: A 30 Years Overview with Bibliometric Analysis Review

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    In this paper, a critical bibliometric analysis study is conducted, coupled with an extensive literature survey on recent developments and associated applications in machine learning research with a perspective on Africa. The presented bibliometric analysis study consists of 2761 machine learning-related documents, of which 98% were articles with at least 482 citations published in 903 journals during the past 30 years. Furthermore, the collated documents were retrieved from the Science Citation Index EXPANDED, comprising research publications from 54 African countries between 1993 and 2021. The bibliometric study shows the visualization of the current landscape and future trends in machine learning research and its application to facilitate future collaborative research and knowledge exchange among authors from different research institutions scattered across the African continent

    Learning disentangled speech representations

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    A variety of informational factors are contained within the speech signal and a single short recording of speech reveals much more than the spoken words. The best method to extract and represent informational factors from the speech signal ultimately depends on which informational factors are desired and how they will be used. In addition, sometimes methods will capture more than one informational factor at the same time such as speaker identity, spoken content, and speaker prosody. The goal of this dissertation is to explore different ways to deconstruct the speech signal into abstract representations that can be learned and later reused in various speech technology tasks. This task of deconstructing, also known as disentanglement, is a form of distributed representation learning. As a general approach to disentanglement, there are some guiding principles that elaborate what a learned representation should contain as well as how it should function. In particular, learned representations should contain all of the requisite information in a more compact manner, be interpretable, remove nuisance factors of irrelevant information, be useful in downstream tasks, and independent of the task at hand. The learned representations should also be able to answer counter-factual questions. In some cases, learned speech representations can be re-assembled in different ways according to the requirements of downstream applications. For example, in a voice conversion task, the speech content is retained while the speaker identity is changed. And in a content-privacy task, some targeted content may be concealed without affecting how surrounding words sound. While there is no single-best method to disentangle all types of factors, some end-to-end approaches demonstrate a promising degree of generalization to diverse speech tasks. This thesis explores a variety of use-cases for disentangled representations including phone recognition, speaker diarization, linguistic code-switching, voice conversion, and content-based privacy masking. Speech representations can also be utilised for automatically assessing the quality and authenticity of speech, such as automatic MOS ratings or detecting deep fakes. The meaning of the term "disentanglement" is not well defined in previous work, and it has acquired several meanings depending on the domain (e.g. image vs. speech). Sometimes the term "disentanglement" is used interchangeably with the term "factorization". This thesis proposes that disentanglement of speech is distinct, and offers a viewpoint of disentanglement that can be considered both theoretically and practically

    Image classification over unknown and anomalous domains

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    A longstanding goal in computer vision research is to develop methods that are simultaneously applicable to a broad range of prediction problems. In contrast to this, models often perform best when they are specialized to some task or data type. This thesis investigates the challenges of learning models that generalize well over multiple unknown or anomalous modes and domains in data, and presents new solutions for learning robustly in this setting. Initial investigations focus on normalization for distributions that contain multiple sources (e.g. images in different styles like cartoons or photos). Experiments demonstrate the extent to which existing modules, batch normalization in particular, struggle with such heterogeneous data, and a new solution is proposed that can better handle data from multiple visual modes, using differing sample statistics for each. While ideas to counter the overspecialization of models have been formulated in sub-disciplines of transfer learning, e.g. multi-domain and multi-task learning, these usually rely on the existence of meta information, such as task or domain labels. Relaxing this assumption gives rise to a new transfer learning setting, called latent domain learning in this thesis, in which training and inference are carried out over data from multiple visual domains, without domain-level annotations. Customized solutions are required for this, as the performance of standard models degrades: a new data augmentation technique that interpolates between latent domains in an unsupervised way is presented, alongside a dedicated module that sparsely accounts for hidden domains in data, without requiring domain labels to do so. In addition, the thesis studies the problem of classifying previously unseen or anomalous modes in data, a fundamental problem in one-class learning, and anomaly detection in particular. While recent ideas have been focused on developing self-supervised solutions for the one-class setting, in this thesis new methods based on transfer learning are formulated. Extensive experimental evidence demonstrates that a transfer-based perspective benefits new problems that have recently been proposed in anomaly detection literature, in particular challenging semantic detection tasks

    Supernatural crossing in Republican Chinese fiction, 1920s–1940s

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    This dissertation studies supernatural narratives in Chinese fiction from the mid-1920s to the 1940s. The literary works present phenomena or elements that are or appear to be supernatural, many of which remain marginal or overlooked in Sinophone and Anglophone academia. These sources are situated in the May Fourth/New Culture ideological context, where supernatural narratives had to make way for the progressive intellectuals’ literary realism and their allegorical application of supernatural motifs. In the face of realism, supernatural narratives paled, dismissed as impractical fantasies that distract one from facing and tackling real life. Nevertheless, I argue that the supernatural narratives do not probe into another mystical dimension that might co-exist alongside the empirical world. Rather, they imagine various cases of the characters’ crossing to voice their discontent with contemporary society or to reflect on the notion of reality. “Crossing” relates to characters’ acts or processes of trespassing the boundary that separates the supernatural from the conventional natural world, thus entailing encounters and interaction between the natural and the supernatural. The dissertation examines how crossing, as a narrative device, disturbs accustomed and mundane situations, releases hidden tensions, and discloses repressed truths in Republican fiction. There are five types of crossing in the supernatural narratives. Type 1 is the crossing into “haunted” houses. This includes (intangible) human agency crossing into domestic spaces and revealing secrets and truths concealed by the scary, feigned ‘haunting’, thus exposing the hidden evil and the other house occupiers’ silenced, suffocated state. Type 2 is men crossing into female ghosts’ apparitional residences. The female ghosts allude to heart-breaking, traumatic experiences in socio-historical reality, evoking sympathetic concern for suffering individuals who are caught in social upheavals. Type 3 is the crossing from reality into the characters’ delusional/hallucinatory realities. While they physically remain in the empirical world, the characters’ abnormal perceptions lead them to exclusive, delirious, and quasi-supernatural experiences of reality. Their crossings blur the concrete boundaries between the real and the unreal on the mental level: their abnormal perceptions construct a significant, meaningful reality for them, which may be as real as the commonly regarded objective reality. Type 4 is the crossing into the netherworld modelled on the real world in the authors’ observation and bears a spectrum of satirised objects of the Republican society. The last type is immortal visitors crossing into the human world. This type satirises humanity’s vices and destructive potential. The primary sources demonstrate their writers’ witty passion to play with super--natural notions and imagery (such as ghosts, demons, and immortals) and stitch them into vivid, engaging scenes using techniques such as the gothic, the grotesque, and the satirical, in order to evoke sentiments such as terror, horror, disgust, dis--orientation, or awe, all in service of their insights into realist issues. The works also creatively tailor traditional Chinese modes and motifs, which exemplifies the revival of Republican interest in traditional cultural heritage. The supernatural narratives may amaze or disturb the reader at first, but what is more shocking, unpleasantly nudging, or thought-provoking is the problematic society and people’s lives that the supernatural (misunderstandings) eventually reveals. They present a more compre--hensive treatment of reality than Republican literature with its revolutionary consciousness surrounding class struggle. The critical perspectives of the supernatural narratives include domestic space, unacknowledged history and marginal individuals, abnormal mentality, and pervasive weaknesses in humanity. The crossing and supernatural narratives function as a means of better understanding the lived reality. This study gathers diverse primary sources written by Republican writers from various educational and political backgrounds and interprets them from a rare perspective, thus filling a research gap. It promotes a fuller view of supernatural narratives in twentieth-century Chinese literature. In terms of reflecting the social and personal reality of the Republican era, the supernatural narratives supplement the realist fiction of the time

    Predictive Maintenance of Critical Equipment for Floating Liquefied Natural Gas Liquefaction Process

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    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
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