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    Undergraduate Catalog of Studies, 2023-2024

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    Towards the Integrated Management of the Texas Citrus Mite Eutetranychus Banksi (Acari: Tetranychidae) in Spain

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    [ES] El ácaro de Texas, Eutetranychus banksi McGregor, es nativo de América y está ampliamente distribuido por el cultivo de cítricos de este continente. En 2013, esta especie se detectó en el sur de la provincia de Valencia, afectando a la principal zona citrícola de España. El ácaro produce graves daños reduciendo la fotosíntesis, causando defoliación y decoloración de los frutos, lo que podría afectar a su valor económico. En primer lugar, este trabajo evaluó el impacto ecológico producido por la especie invasora sobre las especies de ácaros tetraníquidos residentes en los cítricos valencianos Panonychus citri (McGregor) y Eutetranychus orientalis (Klein). Desde su llegada en 2013, E. banksi se ha convertido en el tetraníquido más frecuente y abundante en cítricos desplazando competitivamente a las otras especies, reduciendo su presencia y distribución geográfica, a su vez influenciada por su historia de colonización. En segundo lugar, este trabajo estudió la distribución dentro del árbol y las tendencias estacionales de la plaga y de los ácaros fitoseidos asociados, que pueden contribuir a su control. El ácaro de Texas se alimentó principalmente en la cara adaxial (haz) de las hojas en la periferia de la copa del árbol, mostrando un único pico de población a finales de verano-principios de otoño, mientras que los fitoseidos prefirieron la cara abaxial (envés) de las hojas del interior de la copa, mostrando dos picos, uno principal en primavera y otro menos abundante en otoño. Euseius stipulatus (Athias-Henriot) fue el fitoseido más frecuente y abundante, y cuando E. banksi aumentó se desplazó a las caras adaxiales de las hojas de la parte exterior de la copa y los frutos para alimentarse de su presa, cambiando su coloración de blanco a marrón rojizo evidenciando su contribución al control biológico de la plaga. Sin embargo, no fue capaz de mantener las poblaciones de E. banksi bajo densidades tolerables debido a la desfavorable relación depredador/presa que existe en verano y principios de otoño. En tercer lugar, este trabajo pretendió describir la estructura de población del ácaro de Texas y definir un plan de muestreo. Existieron diferencias en la estructura de edad en frutos y hojas, así como entre hojas de diferentes brotes. Además, a lo largo del tiempo se observaron fluctuaciones en su composición correlacionadas con variaciones en la proporción de sexos. No se observaron diferencias de agregación entre estratos vegetales, pero sí entre estadios inmaduros y adultos, siendo las hembras las menos agregadas. La alta correlación de la población total con las formas móviles y las hembras permitió utilizar ambas como estadio de referencia para el desarrollo del plan de muestreo, estableciendo un muestreo de presencia/ausencia de 100 hojas para las hembras o 400 hojas para las formas móviles. Finalmente, un ácaro fitoseido recientemente descrito, Neoseiulus madeirensis Papadoulis & Kapaxidi, se encontró asociado a E. banksi, sugiriendo que podría ser un candidato prometedor para su control biológico. En cuarto lugar, este trabajo pretendió evaluar el potencial de este depredador para controlar las poblaciones de la plaga. Neoseiulus madeirensis mostró un rápido desarrollo y altas tasas de supervivencia y reproducción alimentándose de E. banksi. El depredador se alimentó principalmente de estadios inmaduros, siendo los huevos el estadio preferido, y mostrando una respuesta funcional de tipo II para todos los estadios de presa ensayados, que se estabilizó a altas densidades de presa con una elevada puesta de huevos. Los valores de supervivencia, reproducción y depredación han sido los mejores obtenidos hasta el momento para cualquier fitoseido ensayado previamente contra E. banksi, lo convierte en un candidato idóneo para el desarrollo de un programa de control biológico basado en sueltas aumentativas, o en la importación con vistas al establecimiento de poblaciones permanentes en cítricos.[CA] L'àcar de Texas, Eutetranychus banksi McGregor, és nadiu d' Amèrica i està àmpliament distribuït pel cultiu de cítrics d'aquest continent. El 2013, esta espècie es va detectar al sud de la província de València, afectant la principal zona citrícola d'Espanya. L'àcar produeix greus danys reduint la fotosíntesi, causant defoliació i decoloració dels fruits, cosa que podria afectar el seu valor econòmic. En primer lloc, aquest treball va avaluar l'impacte ecològic produït per l'espècie invasora sobre les espècies d'àcars tetraníquids residents als cítrics valencians Panonychus citri (McGregor) i Eutetranychus orientalis (Klein). Des de la seva arribada el 2013, E. banksi s'ha convertit en el tetraníquid més freqüent i abundant en cítrics desplaçant competitivament les altres espècies, reduint la seva presència i distribució geogràfica, alhora influenciat per la seva història de colonització. En segon lloc, aquest treball va estudiar la distribució dins de l'arbre i les tendències estacionals de la plaga i dels àcars fitoseids associats, que poden contribuir al seu control. L'àcar de Texas es va alimentar principalment en la cara adaxial (fes) de les fulles a la perifèria de la copa de l'arbre, mostrant un únic pic de població a finals d'estiu-principis de tardor, mentre que els fitoseids van preferir la cara abaxial (revers) de les fulles de l'interior de la copa, mostrant dos pics, un de principal a la primavera i un altre menys abundant a la tardor. Euseius stipulatus (Athias-Henriot) va ser el fitoseid més freqüent i abundant, i quan E. banksi va augmentar es va desplaçar a les cares adaxials de les fulles de la part exterior de la copa i els fruits per alimentar-se de la presa, canviant la seva coloració de blanc a marró vermellós evidenciant la seva contribució al control biològic de la plaga. Tot i això, no va ser capaç de mantenir les poblacions d'E. banksi sota densitats tolerables a causa de la desfavorable relació depredador/presa que hi ha a l'estiu i principis de tardor. En tercer lloc, aquest treball va voler descriure l'estructura de població de l'àcar de Texas i definir un pla de mostreig. Hi hagué diferències en l'estructura d'edat de fruits i fulles, així com entre fulles de diferents brots. A més, al llarg del temps es van observar fluctuacions en la composició correlacionades amb variacions en la proporció de sexes. No es van observar diferències d'agregació entre estrats vegetals, però sí entre estadis immadurs i adults, sent les femelles les menys agregades. L'alta correlació de la població total amb les formes mòbils i les femelles va permetre utilitzar totes dues com a estadi de referència per al desenvolupament del pla de mostreig, establint un mostreig de presència/absència de 100 fulls per a les femelles o 400 fulls per a les formes mòbils. Finalment, una espècie fitoseid recentment descrit, Neoseiulus madeirensis Papadoulis & Kapaxidi, es va trobar associat a E. banksi, suggerint que podria ser un candidat prometedor per al seu control biològic. En quart lloc, aquest treball va voler avaluar el potencial d'aquest depredador per controlar les poblacions de la plaga. Neoseiulus madeirensis va mostrar un desenvolupament ràpid i altes taxes de supervivència i reproducció alimentant-se d'E. banksi. El depredador es va alimentar principalment d'estadis immadurs, sent els ous l'estadi preferit, i mostrant una resposta funcional de tipus II per a tots els estadis de presa assajats, que es va estabilitzar a altes densitats de presa amb una posta d'ous elevada. Els valors de supervivencia, depredació i reproducció han estat els millors obtinguts fins ara per a qualsevol espècie de fitoseid assajat prèviament contra E. banksi, cosa que el converteix en un candidat idoni per al desenvolupament d'un programa de control biològic basat en soltes augmentatives, o en la importació amb vista a l'establiment de poblacions permanents en cítrics.[EN] The Texas citrus mite, Eutetranychus banksi McGregor, is native to the Americas and widely distributed across this continent. In 2013 it was detected in the south of the province of Valencia, affecting the main citrus-growing area in Spain. The mite produces severe damage, reducing photosynthesis, causing defoliation, and producing a lack in fruit pigmentation, which could affect its economic value. Firstly, this work evaluated the ecological impact produced by the invasive species on the resident spider mites Panonychus citri (McGregor) and Eutetranychus orientalis (Klein). Since its arrival in 2013, E. banksi has become the most frequent and abundant spider mite on citrus, competitively displacing the other species and reducing their presence and geographic range, which is influenced by its colonisation history. Secondly, this work studies the within-tree distribution and seasonal trends of the pest and associated phytoseiid mites, which may contribute to its control. The Texas citrus mite was feeding mainly on the adaxial (upper) side of leaves in the periphery of the tree canopy showing a single population peak in late summer-early autumn, while phytoseiids preferred the abaxial (lower) sides inside the canopy showing two peaks, a main spring peak and a second, less abundant, in autumn. Euseius stipulatus (Athias-Henriot) was the most frequent and abundant phytoseiid, and when E. banksi increased, it moved to the adaxial sides on outer leaves and fruits to feed on its prey and changed its colouring from white to reddish-brown, evidencing its contribution to biological pest control. However, it was not capable of maintaining E. banksi populations under tolerable densities due to the unfavourable predator/prey ratios in summer and early autumn. Thirdly, this work aimed to describe the pest population structure and define a sampling plan. There were differences in the age structure on fruits and leaves, as well as between leaves from different flushes. Furthermore, over time, there were fluctuations in its composition correlated with variations in sex-ratio. No aggregation differences among plant strata were found, but there were significant differences between immature and adult stages, the females being the less aggregated. The high correlation of the total population with the motile forms and females allowed the use of both as a reference stage in the sampling plan, establishing a presence/absence sampling of 100 leaves for females or 400 leaves for motile forms. Finally, a recently described phytoseiid mite, Neoseiulus madeirensis Papadoulis & Kapaxidi, was found to be associated with E. banksi, suggesting that it could be a promising candidate for pest suppression. Fourthly, this work aimed to evaluate the potential of this predator to control pest populations. Neoseiulus madeirensis exhibited a short developmental time, high survival and reproductive rates feeding on E. banksi. The predator was fed mainly on immature stages, with eggs being the preferred stage, showing a type II functional response for all the prey stages tested, that stabilises at high prey densities with high egg laying. Survival, predation and reproduction values were the best obtained so far for any phytoseiid previously tested against E. banksi, making it a suitable candidate for the development of a biological control program based on augmentative releases, or importation aiming for the establishment of permanent populations on citrus.López Olmos, S. (2023). Towards the Integrated Management of the Texas Citrus Mite Eutetranychus Banksi (Acari: Tetranychidae) in Spain [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/20155

    Determining traffic accident patterns through clustering and direct count

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    This research focuses on the detailed analysis of traffic accident patterns in an urban context, uti- lizing advanced data classification techniques. Through a systematic approach and the imple- mentation of multiple studies, the project explores how the combination of different variables helps to the identification of patterns. Initially, the study employs a clustering method to identify accident patterns within a range of variables. This approach is complemented by an analysis based on a counting method that has been developed which allows for a more direct and precise classification based on the frequency of specific variable combinations. The results with both methods reveal significant patterns, that help understand the relation between the variables that are studied, for example, the prevalence of accidents involving mo- torcycles and their correlation with certain districts and times of the day. The research compares and evaluates the effectiveness of both classification methods, highlight- ing their strengths and limitations. While the clustering method provides a comprehensive and detailed overview of accident patterns, the counting method offers exceptional precision and speed in identifying the most frequent combinations of categories within a large group of vari- ables. In conclusion, this study provides valuable insights for designing accident prevention strategies and improving road safety measures. The combination of classification and counting methods, thorough the pattern identification, emerges as a powerful tool for understanding the dynamics of traffic accidents and contributing to urban safet

    Population genetic structure of the provincially endangered mainland Eastern Moose (Alces americanus americanus) in Nova Scotia, Canada

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    Eastern Moose (Alces americanus americanus (Clinton, 1822)) on mainland Nova Scotia (MNS) are declining and experience limited immigration across the Isthmus of Chignecto from the larger population in neighbouring New Brunswick. Provincially Endangered, the recovery strategy for MNS Moose involves mitigating various threats that may lead to local extirpation. We examine genetic diversity of MNS Moose using microsatellite markers and mitochondrial (mtDNA) control region sequences. Genetic similarities with the Alces a. americana population in New Brunswick and the introduced Northwestern Moose (Alces americanus andersoni (= Alces alces andersoni) Peterson, 1952) population on Cape Breton Island are also analysed. Observed heterozygosity for microsatellites for MNS Moose was low and there was also evidence of limited gene flow between Nova Scotia and New Brunswick across the narrow Isthmus of Chignecto that connects these provinces. Consistent with relatively recent colonization of North America by Moose dispersing across the Bering Land Bridge <15 000 years ago, mtDNA haplotypes of MNS Moose were identical or extremely similar to haplotypes found across North America. However, mtDNA diversity was lower in Nova Scotia and New Brunswick than in more central regions of the species’ range. Active measures to maintain habitat that promote connectivity across the Isthmus of Chignecto would likely be valuable for Moose in terms of maintaining genetic variation in the region and reducing inbreeding

    Online semi-supervised learning in non-stationary environments

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    Existing Data Stream Mining (DSM) algorithms assume the availability of labelled and balanced data, immediately or after some delay, to extract worthwhile knowledge from the continuous and rapid data streams. However, in many real-world applications such as Robotics, Weather Monitoring, Fraud Detection Systems, Cyber Security, and Computer Network Traffic Flow, an enormous amount of high-speed data is generated by Internet of Things sensors and real-time data on the Internet. Manual labelling of these data streams is not practical due to time consumption and the need for domain expertise. Another challenge is learning under Non-Stationary Environments (NSEs), which occurs due to changes in the data distributions in a set of input variables and/or class labels. The problem of Extreme Verification Latency (EVL) under NSEs is referred to as Initially Labelled Non-Stationary Environment (ILNSE). This is a challenging task because the learning algorithms have no access to the true class labels directly when the concept evolves. Several approaches exist that deal with NSE and EVL in isolation. However, few algorithms address both issues simultaneously. This research directly responds to ILNSE’s challenge in proposing two novel algorithms “Predictor for Streaming Data with Scarce Labels” (PSDSL) and Heterogeneous Dynamic Weighted Majority (HDWM) classifier. PSDSL is an Online Semi-Supervised Learning (OSSL) method for real-time DSM and is closely related to label scarcity issues in online machine learning. The key capabilities of PSDSL include learning from a small amount of labelled data in an incremental or online manner and being available to predict at any time. To achieve this, PSDSL utilises both labelled and unlabelled data to train the prediction models, meaning it continuously learns from incoming data and updates the model as new labelled or unlabelled data becomes available over time. Furthermore, it can predict under NSE conditions under the scarcity of class labels. PSDSL is built on top of the HDWM classifier, which preserves the diversity of the classifiers. PSDSL and HDWM can intelligently switch and adapt to the conditions. The PSDSL adapts to learning states between self-learning, micro-clustering and CGC, whichever approach is beneficial, based on the characteristics of the data stream. HDWM makes use of “seed” learners of different types in an ensemble to maintain its diversity. The ensembles are simply the combination of predictive models grouped to improve the predictive performance of a single classifier. PSDSL is empirically evaluated against COMPOSE, LEVELIW, SCARGC and MClassification on benchmarks, NSE datasets as well as Massive Online Analysis (MOA) data streams and real-world datasets. The results showed that PSDSL performed significantly better than existing approaches on most real-time data streams including randomised data instances. PSDSL performed significantly better than ‘Static’ i.e. the classifier is not updated after it is trained with the first examples in the data streams. When applied to MOA-generated data streams, PSDSL ranked highest (1.5) and thus performed significantly better than SCARGC, while SCARGC performed the same as the Static. PSDSL achieved better average prediction accuracies in a short time than SCARGC. The HDWM algorithm is evaluated on artificial and real-world data streams against existing well-known approaches such as the heterogeneous WMA and the homogeneous Dynamic DWM algorithm. The results showed that HDWM performed significantly better than WMA and DWM. Also, when recurring concept drifts were present, the predictive performance of HDWM showed an improvement over DWM. In both drift and real-world streams, significance tests and post hoc comparisons found significant differences between algorithms, HDWM performed significantly better than DWM and WMA when applied to MOA data streams and 4 real-world datasets Electric, Spam, Sensor and Forest cover. The seeding mechanism and dynamic inclusion of new base learners in the HDWM algorithms benefit from the use of both forgetting and retaining the models. The algorithm also provides the independence of selecting the optimal base classifier in its ensemble depending on the problem. A new approach, Envelope-Clustering is introduced to resolve the cluster overlap conflicts during the cluster labelling process. In this process, PSDSL transforms the centroids’ information of micro-clusters into micro-instances and generates new clusters called Envelopes. The nearest envelope clusters assist the conflicted micro-clusters and successfully guide the cluster labelling process after the concept drifts in the absence of true class labels. PSDSL has been evaluated on real-world problem ‘keystroke dynamics’, and the results show that PSDSL achieved higher prediction accuracy (85.3%) and SCARGC (81.6%), while the Static (49.0%) significantly degrades the performance due to changes in the users typing pattern. Furthermore, the predictive accuracies of SCARGC are found highly fluctuated between (41.1% to 81.6%) based on different values of parameter ‘k’ (number of clusters), while PSDSL automatically determine the best values for this parameter

    Producing context specific land cover and land use maps of human-modified tropical forest landscapes for infectious disease applications

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    Satellite-based land cover mapping plays an important role in understanding changes in ecosystems and biodiversity. There are global land cover products available, however for region specific studies of drivers of infectious disease patterns, these can lack the spatial and thematic detail or accuracy required to capture key ecological processes. To overcome this, we produced our own Landsat derived 30 m maps for three districts in India's Western Ghats (Wayanad, Shivamogga and Sindhudurg). The maps locate natural vegetation types, plantation types, agricultural areas, water bodies and settlements in the landscape, all relevant to functional resource use of species involved in infectious disease dynamics. The maps represent the mode of 50 classification iterations and include a spatial measure of class stability derived from these iterations. Overall accuracies for Wayanad, Shivamogga and Sindhudurg are 94.7 % (SE 1.2 %), 88.9 % (SE 1.2 %) and 88.8 % (SE 2 %) respectively. Class classification stability was high across all three districts and the individual classes that matter for defining key interfaces between human habitation, forests, crop, and plantation cultivation, were generally well separated. A comparison with the 300 m global ESA CCI land cover map highlights lower ESA CCI class accuracies and the importance of increased spatial resolution when dealing with complex landscape mosaics. A comparison with the 30 m Global Forest Change product reveals an accurate mapping of forest loss and different dynamics between districts (i.e., Forests lost to Built-up versus Forests lost to Plantations), demonstrating an interesting complementarity between our maps and the % tree cover Global Forest Change product. When studying infectious disease responses to land use change in tropical forest ecosystems, we recommend using bespoke land cover/use classifications reflecting functional resource use by relevant vectors, reservoirs, and people. Alternatively, global products should be carefully validated with ground reference points representing locally relevant habitats. [Abstract copyright: Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.

    Multidisciplinary perspectives on Artificial Intelligence and the law

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    This open access book presents an interdisciplinary, multi-authored, edited collection of chapters on Artificial Intelligence (‘AI’) and the Law. AI technology has come to play a central role in the modern data economy. Through a combination of increased computing power, the growing availability of data and the advancement of algorithms, AI has now become an umbrella term for some of the most transformational technological breakthroughs of this age. The importance of AI stems from both the opportunities that it offers and the challenges that it entails. While AI applications hold the promise of economic growth and efficiency gains, they also create significant risks and uncertainty. The potential and perils of AI have thus come to dominate modern discussions of technology and ethics – and although AI was initially allowed to largely develop without guidelines or rules, few would deny that the law is set to play a fundamental role in shaping the future of AI. As the debate over AI is far from over, the need for rigorous analysis has never been greater. This book thus brings together contributors from different fields and backgrounds to explore how the law might provide answers to some of the most pressing questions raised by AI. An outcome of the Católica Research Centre for the Future of Law and its interdisciplinary working group on Law and Artificial Intelligence, it includes contributions by leading scholars in the fields of technology, ethics and the law.info:eu-repo/semantics/publishedVersio

    iBUST: An intelligent behavioural trust model for securing industrial cyber-physical systems

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    To meet the demand of the world's largest population, smart manufacturing has accelerated the adoption of smart factories—where autonomous and cooperative instruments across all levels of production and logistics networks are integrated through a Cyber-Physical Production System (CPPS). However, these networks are comprised of various heterogeneous devices with varying computational power and memory capabilities. As a result, many secure communication protocols – that demand considerably high computational power and memory – can not be verbatim employed on these networks, and thereby, leaving them more vulnerable to security threats and attacks over conventional networks. These threats can largely be tackled by employing a Trust Management Model (TMM) by exploiting the behavioural patterns of nodes to identify their trust class. In this context, ML-based models are best suited due to their ability to capture hidden patterns in data, learning and improving the pattern detection accuracy over time to counteract and tackle threats of a dynamic nature, which is absent in most of the conventional models. However, among the existing ML-based solutions in detecting attack patterns, many of them are computationally expensive, require a long training time, and a considerably large amount of training data—which are seldom available. An aid to this is the association rule learning (ARL) paradigm, whose models are computationally inexpensive and do not require a long training time. Therefore, this paper proposes an ARL-based intelligent Behavioural Trust Model (iBUST) for securing the CPPS. For this intelligent TMM, a variant of Frequency Pattern Growth (FP-Growth), called enhanced FP-Growth (EFP-Growth) algorithm is developed by altering the internal data structures for faster execution and by developing a modified exponential decay function (MEDF) to automatically calculate minimum supports for adapting trust evolution characteristics. In addition, a new optimisation model for finding optimum parameter values in the MEDF and an algorithm for transmuting a 1D quantitative feature into a respective categorical feature are developed to facilitate the model. Afterwards, the trust class of an object is identified employing the Naïve Bayes classifier. This proposed model is evaluated on a trust evolution-supported experimental environment along with other compared models taking a benchmark dataset into consideration, where it outperforms its counterparts
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