3,550 research outputs found

    From transients to permanent residents: the existence of obligate aerobic microorganisms in the goat rumen

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    The rumen serves as a complex ecosystem, harboring diverse microbial communities that play crucial ecological roles. Because previous studies have predominantly focused on anaerobic microorganisms, limited attention has been given to aerobic microorganisms in the goat rumen. This study aims to explore the diversity of aerobic microorganisms in the rumen and understand their niche and ecological roles. Rumen fluid samples were collected from 6 goats at different time points post-morning feeding. pH, NH3-N, and volatile fatty acid (TVFA) concentrations were measured, while In vitro cultivation of aerobic microorganisms was performed using PDA medium. Internal Transcribed Spacer (ITS) and 16S sequencing unveiled microbial diversity within the rumen fluid samples. Evidence of obligate aerobic microorganisms in the goat rumen suggests their potential contribution to ecological functionalities. Significantly, certain aerobic microorganisms exhibited correlations with TVFA levels, implying their involvement in TVFA metabolism. This study provides evidence of the existence and potential ecological roles of obligate aerobic microorganisms in the goat rumen. The findings underscore the significance of comprehensively deciphering goat rumen microbial communities and their interactions, with aerobes regarded as permanent residents rather than transients. These insights form a solid foundation for advancing our understanding of the intricate interplay between goat and their aerobic microorganisms in the rumen

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    La traduzione specializzata all’opera per una piccola impresa in espansione: la mia esperienza di internazionalizzazione in cinese di Bioretics© S.r.l.

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    Global markets are currently immersed in two all-encompassing and unstoppable processes: internationalization and globalization. While the former pushes companies to look beyond the borders of their country of origin to forge relationships with foreign trading partners, the latter fosters the standardization in all countries, by reducing spatiotemporal distances and breaking down geographical, political, economic and socio-cultural barriers. In recent decades, another domain has appeared to propel these unifying drives: Artificial Intelligence, together with its high technologies aiming to implement human cognitive abilities in machinery. The “Language Toolkit – Le lingue straniere al servizio dell’internazionalizzazione dell’impresa” project, promoted by the Department of Interpreting and Translation (ForlĂŹ Campus) in collaboration with the Romagna Chamber of Commerce (ForlĂŹ-Cesena and Rimini), seeks to help Italian SMEs make their way into the global market. It is precisely within this project that this dissertation has been conceived. Indeed, its purpose is to present the translation and localization project from English into Chinese of a series of texts produced by Bioretics© S.r.l.: an investor deck, the company website and part of the installation and use manual of the Aliquis© framework software, its flagship product. This dissertation is structured as follows: Chapter 1 presents the project and the company in detail; Chapter 2 outlines the internationalization and globalization processes and the Artificial Intelligence market both in Italy and in China; Chapter 3 provides the theoretical foundations for every aspect related to Specialized Translation, including website localization; Chapter 4 describes the resources and tools used to perform the translations; Chapter 5 proposes an analysis of the source texts; Chapter 6 is a commentary on translation strategies and choices

    Simultaneous Reconstruction of Duplication Episodes and Gene-Species Mappings

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    We present a novel problem, called MetaEC, which aims to infer gene-species assignments in a collection of gene trees with missing labels by minimizing the size of duplication episode clustering (EC). This problem is particularly relevant in metagenomics, where incomplete data often poses a challenge in the accurate reconstruction of gene histories. To solve MetaEC, we propose a polynomial time dynamic programming (DP) formulation that verifies the existence of a set of duplication episodes from a predefined set of episode candidates. We then demonstrate how to use DP to design an algorithm that solves MetaEC. Although the algorithm is exponential in the worst case, we introduce a heuristic modification of the algorithm that provides a solution with the knowledge that it is exact. To evaluate our method, we perform two computational experiments on simulated and empirical data containing whole genome duplication events, showing that our algorithm is able to accurately infer the corresponding events

    UNGOML: Automated Classification of unsafe Usages in Go

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    The Go programming language offers strong protection from memory corruption. As an escape hatch of these protections, it provides the unsafe package. Previous studies identified that this unsafe package is frequently used in real-world code for several purposes, e.g., serialization or casting types. Due to the variety of these reasons, it may be possible to refactor specific usages to avoid potential vulnerabilities. However, the classification of unsafe usages is challenging and requires the context of the call and the program's structure. In this paper, we present the first automated classifier for unsafe usages in Go, UNGOML, to identify what is done with the unsafe package and why it is used. For UNGOML, we built four custom deep learning classifiers trained on a manually labeled data set. We represent Go code as enriched control-flow graphs (CFGs) and solve the label prediction task with one single-vertex and three context-aware classifiers. All three context-aware classifiers achieve a top-1 accuracy of more than 86% for both dimensions, WHAT and WHY. Furthermore, in a set-valued conformal prediction setting, we achieve accuracies of more than 93% with mean label set sizes of 2 for both dimensions. Thus, UNGOML can be used to efficiently filter unsafe usages for use cases such as refactoring or a security audit. UNGOML: https://github.com/stg-tud/ungoml Artifact: https://dx.doi.org/10.6084/m9.figshare.22293052Comment: 13 pages, accepted at the 2023 IEEE/ACM 20th International Conference on Mining Software Repositories (MSR 2023

    Tiny Machine Learning Environment: Enabling Intelligence on Constrained Devices

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    Running machine learning algorithms (ML) on constrained devices at the extreme edge of the network is problematic due to the computational overhead of ML algorithms, available resources on the embedded platform, and application budget (i.e., real-time requirements, power constraints, etc.). This required the development of specific solutions and development tools for what is now referred to as TinyML. In this dissertation, we focus on improving the deployment and performance of TinyML applications, taking into consideration the aforementioned challenges, especially memory requirements. This dissertation contributed to the construction of the Edge Learning Machine environment (ELM), a platform-independent open-source framework that provides three main TinyML services, namely shallow ML, self-supervised ML, and binary deep learning on constrained devices. In this context, this work includes the following steps, which are reflected in the thesis structure. First, we present the performance analysis of state-of-the-art shallow ML algorithms including dense neural networks, implemented on mainstream microcontrollers. The comprehensive analysis in terms of algorithms, hardware platforms, datasets, preprocessing techniques, and configurations shows similar performance results compared to a desktop machine and highlights the impact of these factors on overall performance. Second, despite the assumption that TinyML only permits models inference provided by the scarcity of resources, we have gone a step further and enabled self-supervised on-device training on microcontrollers and tiny IoT devices by developing the Autonomous Edge Pipeline (AEP) system. AEP achieves comparable accuracy compared to the typical TinyML paradigm, i.e., models trained on resource-abundant devices and then deployed on microcontrollers. Next, we present the development of a memory allocation strategy for convolutional neural networks (CNNs) layers, that optimizes memory requirements. This approach reduces the memory footprint without affecting accuracy nor latency. Moreover, e-skin systems share the main requirements of the TinyML fields: enabling intelligence with low memory, low power consumption, and low latency. Therefore, we designed an efficient Tiny CNN architecture for e-skin applications. The architecture leverages the memory allocation strategy presented earlier and provides better performance than existing solutions. A major contribution of the thesis is given by CBin-NN, a library of functions for implementing extremely efficient binary neural networks on constrained devices. The library outperforms state of the art NN deployment solutions by drastically reducing memory footprint and inference latency. All the solutions proposed in this thesis have been implemented on representative devices and tested in relevant applications, of which results are reported and discussed. The ELM framework is open source, and this work is clearly becoming a useful, versatile toolkit for the IoT and TinyML research and development community

    Cross-cultural patterns in mobile playtime: an analysis of 118 billion hours of human data

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    open access articleDespite the prevalence of gaming as a human activity, the literature on playtime is uninformed by large-scale, high-quality data. This has led to an evidence-base in which the existence of specific cultural gaming cultures (e.g. exceptional levels of gaming in East Asian nations) are not well-supported by evidence. Here we address this evidence gap by conducting the world’s first large-scale investigation of cross-cultural differences in mobile gaming via telemetry analysis. Our data cover 118 billion hours of playtime occurring in 214 countries and regions between October 2020 and October 2021. A cluster analysis establishes a data-driven set of cross-cultural groupings that describe differences in how the world plays mobile games. Despite contemporary arguments regarding Asian exceptionalism in terms of playtime, analysis shows that many East Asian countries (e.g., China) were not highly differentiated from most high-GDP Northern European nations across several measures of play. Instead, a range of previously unstudied and highly differentiated cross-cultural clusters emerged from the data and are presented here, showcasing the diversity of global gaming

    The Center of Excellence in Atmospheric Science (2002–2019) — from molecular and biological processes to the global climate

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    The study of atmospheric processes related to climate requires a multidisciplinary approach, encompassing physics, chemistry, meteorology, forest science, and environmental science. The Academy of Finland Centre of Excellence in atmospheric sciences (CoE ATM) responded to that need for 18 years and produced extensive research and eloquent results, which are summarized in this review. The work in the CoE ATM enhanced our understanding in biogeochemical cycles, ecosystem processes, dynamics of aerosols, ions and neutral clusters in the lower atmosphere, and cloud formation and their interactions and feedbacks. The CoE ATM combined continuous and comprehensive long-term in-situ observations in various environments, ecosystems and platforms, ground- and satellitebased remote sensing, targeted laboratory and field experiments, and advanced multi-scale modeling. This has enabled improved conceptual understanding and quantifications across relevant spatial and temporal scales. Overall, the CoE ATM served as a platform for the multidisciplinary research community to explore the interactions between the biosphere and atmosphere under a common and adaptive framework

    Environment Knowledge-Driven Generic Models to Detect Coughs From Audio Recordings

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    Goal: Millions of people are dying due to res- piratory diseases, such as COVID-19 and asthma, which are often characterized by some common symptoms, including coughing. Therefore, objective reporting of cough symp- toms utilizing environment-adaptive machine-learning models with microphone sensing can directly contribute to respiratory disease diagnosis and patient care. Methods: In this work, we present three generic modeling approaches – unguided, semi-guided, and guided approaches consid- ering three potential scenarios, i.e., when a user has no prior knowledge, some knowledge, and detailed knowledge about the environments, respectively. Results: From detailed analysis with three datasets, we find that guided models are up to 28% more accurate than the unguided models. We find reasonable performance when assessing the applicability of our models using three additional datasets, including two open-sourced cough datasets. Con- clusions: Though guided models outperform other models, they require a better understanding of the environment

    Nanomaterial fate and bioavailability in freshwater environments

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    Given the widespread use of silver nanomaterials (AgNM), their accidental or intentional release into the environment is inevitable. AgNM release into riverine systems is a daily occurrence, and following their release, they will undoubtedly interact with naturally occurring organic and inorganic particulates and sediment interfaces. At this point, AgNM's long-term threat to freshwater ecosystems is unclear. We must develop our understanding of AgNM fate, toxicity, and bioavailability using testing approaches that systematically investigate AgNM environmental interaction within single-factor and multifactor systems. This body of research aimed to comprehensively examine selected AgNM particles that were tracked within parallel fate scenarios and toxicity and bioavailability studies. Results showed contrasting behavior between the two tested AgNM. Findings also demonstrated that low shear flow is a significant factor influencing the flocculation and settling rates of AgNM, which differentially regulated the persistence and residence time of aqueous phase AgNM within simulated riverine systems. Experiments with low shear flow showed a significant increase in AgNM water column removal and modulated the physicochemistry differentially compared to quiescent systems. The findings on the influence of bed sediment interactions with waterborne AgNM demonstrated that they are a vital process that increases the transfer and exchange of AgNM from the water column to the bed. Toxicity studies showed how abiotic factors could modulate toxicity differentially between aquatic species and how inorganic and organic matter can increase and decrease AgNM toxicity. Exposure studies contrasting singular and multifactor exposures with and without low shear flow demonstrated that they modulate the exposure of AgNM significantly differently. In conclusion, the proof-of-concept flume designs for testing the environmental fate and exposure of AgNM showed promise and that, with further refinement, could be further incorporated into the life-cycle testing framework of ENMs, to produce accurate semi-empirical coefficients for environmental models for the assessment of hazard
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