4,343 research outputs found

    A new species of Brachycephalus (Anura: Brachycephalidae) from the northern portion of the state of Rio de Janeiro, southeastern Brazil

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    Abstract Brachycephalus is a genus of small ground-dwelling anurans, endemic to the Brazilian Atlantic Forest. Recent molecular analyses have corroborated the monophyly of three species groups within this genus (B. ephippium, B. ephippium, and B. ephippium). In the meantime, the genus has been targeted as a group with recent taxonomic issues owing to its interspecific morphological similarity and genetic conservatism. Herein, we describe a new species of Brachycephalus from the northern portion of Serra do Mar mountain range, in the state of Rio de Janeiro, Brazil. It belongs to the B. ephippium species group, exhibiting moderate hyperossification of the skull and vertebral column. The new species can be distinguished from all other congeners based on morphological, acoustic, and molecular data. Furthermore, we provide information on osteology and natural history of the new species

    How the Migration Process drives the establishment of a Psychological Home: An Italian mixed methods study in the light of Community Psychology

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    The concept of "home" holds profound significance for individuals, yet its definition becomes complex amid changes in living environments, particularly for migrants. This doctoral project explores how migrants construct their psychological sense of home and its impact on well-being. Defense mechanisms, such as idealization or assimilation, play a pivotal role in shaping migrants' psychological sense of home, evolving over time. Despite recognizing the significance of home in migrants' lives, there is a research gap in understanding their psychological sense of home. The study adopts a community psychology approach to migration research, emphasizing relational aspects, contextual interactions, and intervention development. Italy's social scenario, marked by cultural pluralism, makes this approach particularly relevant. The thesis comprises five chapters, offering a theoretical framework, exploring the concept of home, presenting the research question, describing the Italian context, and detailing the methodology. The main studies include a literature review, qualitative interviews, and a quantitative study, each contributing to a comprehensive understanding of migrants' psychological sense of home. The final chapter integrates findings, elucidating the process of establishing a psychological home for migrants. It explores bridging gaps between individual and community-focused migration studies, highlights study limitations, suggests future research, and outlines practical implications. The study contributes valuable insights into the intricate relationship between migrants' psychological sense of home, well-being, and community interactions

    Quantifying Equity Risk Premia: Financial Economic Theory and High-Dimensional Statistical Methods

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    The overarching question of this dissertation is how to quantify the unobservable risk premium of a stock when its return distribution varies over time. The first chapter, titled “Theory-based versus machine learning-implied stock risk premia”, starts with a comparison of two competing strands of the literature. The approach advocated by Martin and Wagner (2019) relies on financial economic theory to derive a closed-form approximation of conditional risk premia using information embedded in the prices of European options. The other approach, exemplified by the study of Gu et al. (2020), draws on the flexibility of machine learning methods and vast amounts of historical data to determine the unknown functional form. The goal of this study is to determine which of the two approaches produces more accurate measurements of stock risk premia. In addition, we present a novel hybrid approach that employs machine learning to overcome the approximation errors induced by the theory-based approach. We find that our hybrid approach is competitive especially at longer investment horizons. The second chapter, titled “The uncertainty principle in asset pricing”, introduces a representation of the conditional capital asset pricing model (CAPM) in which the betas and the equity premium are jointly characterized by the information embedded in option prices. A unique feature of our model is that its implied components represent valid measurements of their physical counterparts without the need for any further risk adjustment. Moreover, because the model’s time-varying parameters are directly observable, the model can be tested without any of the complications that typically arise from statistical estimation. One of the main empirical findings is that the well-known flat relationship between average predicted and realized excess returns of beta-sorted portfolios can be explained by the uncertainty governing market excess returns. In the third chapter, titled “Multi-task learning in cross-sectional regressions”, we challenge the way in which cross-sectional regressions are used to test factor models with time-varying loadings. More specifically, we extend the procedure by Fama and MacBeth (1973) by systematically selecting stock characteristics using a combination of l1- and l2-regularization, known as the multi-task Lasso, and addressing the bias that is induced by selection via repeated sample splitting. In the empirical part of this chapter, we apply our testing procedure to the option-implied CAPM from chapter two, and find that, while variants of the momentum effect lead to a rejection of the model, the implied beta is by far the most important predictor of cross-sectional return variation

    Breaking Virtual Barriers : Investigating Virtual Reality for Enhanced Educational Engagement

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    Virtual reality (VR) is an innovative technology that has regained popularity in recent years. In the field of education, VR has been introduced as a tool to enhance learning experiences. This thesis presents an exploration of how VR is used from the context of educators and learners. The research employed a mixed-methods approach, including surveying and interviewing educators, and conducting empirical studies to examine engagement, usability, and user behaviour within VR. The results revealed educators are interested in using VR for a wide range of scenarios, including thought exercises, virtual field trips, and simulations. However, they face several barriers to incorporating VR into their practice, such as cost, lack of training, and technical challenges. A subsequent study found that virtual reality can no longer be assumed to be more engaging than desktop equivalents. This empirical study showed that engagement levels were similar in both VR and non-VR environments, suggesting that the novelty effect of VR may be less pronounced than previously assumed. A study against a VR mind mapping artifact, VERITAS, demonstrated that complex interactions are possible on low-cost VR devices, making VR accessible to educators and students. The analysis of user behaviour within this VR artifact showed that quantifiable strategies emerge, contributing to the understanding of how to design for collaborative VR experiences. This thesis provides insights into how the end-users in the education space perceive and use VR. The findings suggest that while educators are interested in using VR, they face barriers to adoption. The research highlights the need to design VR experiences, with understanding of existing pedagogy, that are engaging with careful thought applied to complex interactions, particularly for collaborative experiences. This research contributes to the understanding of the potential of VR in education and provides recommendations for educators and designers to enhance learning experiences using VR

    Ecology of methanotrophs in a landfill methane biofilter

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    Decomposing landfill waste is a significant anthropogenic source of the potent climate-active gas methane (CH₄). To mitigate fugitive methane emissions Norfolk County Council are trialling a landfill biofilter, designed to harness the methane oxidizing potential of methanotrophic bacteria. These methanotrophs can convert CH₄ to CO₂ or biomass and act as CH₄ sinks. The most active CH₄ oxidising regions of the Strumpshaw biofilter were identified from in-situ temperature, CH₄, O₂ and CO₂ profiles. While soil CH₄ oxidation potential was estimated and used to confirm methanotroph activity and determine optimal soil moisture conditions for CH₄ oxidation. It was observed that most CH₄ oxidation occurs in the top 60cm of the biofilter (up to 50% of CH4 input) at temperatures around 50ÂșC, optimal soil moisture was 10-27.5%. A decrease in in-situ temperature following CH₄ supply interruption suggested the high biofilter temperatures were driven by CH₄ oxidation. The biofilter soil bacterial community was profiled by 16S rRNA gene analysis, with methanotrophs accounting for ~5-10% of bacteria. Active methanotrophs at a range of different incubation temperatures were identified by ÂčÂłCH₄ DNA stable-isotope probing coupled with 16S rRNA gene amplicon and metagenome analysis. These methods identified Methylocella, Methylobacter, Methylocystis and Crenothrix as potential CH₄ oxidisers at the lower temperatures (30ÂșC/37ÂșC) observed following system start-up or gas-feed interruption. At higher temperatures typical of established biofilter operation (45ÂșC/50ÂșC), Methylocaldum and an unassigned Methylococcaceae species were the dominant active methanotrophs. Finally, novel methanotrophs Methylococcus capsulatus (Norfolk) and Methylocaldum szegediense (Norfolk) were isolated from biofilter soil enrichments. Methylocaldum szegediense (Norfolk) may be very closely related to or the same species as one of the most abundant active methanotrophs in a metagenome from a 50ÂșC biofilter soil incubation, based on genome-to-MAG similarity. This isolate was capable of growth over a broad temperature range (37-62ÂșC) including the higher (in-situ) biofilter temperatures (>50ÂșC)

    Reinforcement Learning with General Utilities: Simpler Variance Reduction and Large State-Action Space

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    We consider the reinforcement learning (RL) problem with general utilities which consists in maximizing a function of the state-action occupancy measure. Beyond the standard cumulative reward RL setting, this problem includes as particular cases constrained RL, pure exploration and learning from demonstrations among others. For this problem, we propose a simpler single-loop parameter-free normalized policy gradient algorithm. Implementing a recursive momentum variance reduction mechanism, our algorithm achieves O~(ϔ−3)\tilde{\mathcal{O}}(\epsilon^{-3}) and O~(ϔ−2)\tilde{\mathcal{O}}(\epsilon^{-2}) sample complexities for Ï”\epsilon-first-order stationarity and Ï”\epsilon-global optimality respectively, under adequate assumptions. We further address the setting of large finite state action spaces via linear function approximation of the occupancy measure and show a O~(ϔ−4)\tilde{\mathcal{O}}(\epsilon^{-4}) sample complexity for a simple policy gradient method with a linear regression subroutine.Comment: 48 pages, 2 figures, ICML 2023, this paper was initially submitted in January 26th 202

    Examining the Impact of Game-Based Learning on Student Engagement and Performance in an Introductory Computer Programming Course at the University of the Southern Caribbean (USC)

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    At the University of the Southern Caribbean (USC) students often struggle with learning programming. Because of this struggle, they often become disengaged with the programming courses, with some transferring to other degree programmes or withdrawing from the programme. While several strategies have been used to ensure that students can problem- solve, design, and develop coded solutions, it has not been enough to alleviate the issues. Game- based learning (GBL) emerged as a possible strategy that can potentially help students develop these skills while keeping them engaged with the course content. Implementing such a strategy within the department requires evidence that it can be an effective technique for teaching and learning programming. Therefore, the aim of this study is to evaluate the impact of GBL on student engagement and overall performance in an introductory programming course. The research was designed as a deductive exploratory single case study research strategy and method. It approaches the aims and objectives from a pragmatic perspective, and as a result, uses a mixed methodological approach to data collection and analysis. The findings show that while GBL does not alleviate the common negative reactions to learning programming, it does provide a learning environment engaging enough for students to overlook these. This results in students having an enhanced perception of the knowledge and improved performance. In implementing GBL in other programming courses, some features that are potentially the most impactful on students learning are immediate feedback, freedom to fail, user interface, code without limitations, and a visual representation of progress
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