Monitoring water-soil dynamics and tree survival using soil sensors under a big data approach

ArticleThe high importance of green urban planning to ensure access to green areas requires modern and multi-source decision-support tools. The integration of remote sensing data and sensor developments can contribute to the improvement of decision-making in urban forestry. This study proposes a novel big data-based methodology that combines real-time information from soil sensors and climate data to monitor the establishment of a new urban forest in semi-arid conditions. Water-soil dynamics and their implication in tree survival were analyzed considering the application of di erent treatment restoration techniques oriented to facilitate the recovery of tree and shrub vegetation in the degraded area. The synchronized data-capturing scheme made it possible to evaluate hourly, daily, and seasonal changes in soil-water dynamics. The spatial variation of soil-water dynamics was captured by the sensors and it highly contributed to the explanation of the observed ground measurements on tree survival. The methodology showed how the e ciency of treatments varied depending on species selection and across the experimental design. The use of retainers for improving soil moisture content and adjusting tree-watering needs was, on average, the most successful restoration technique. The results and the applied calibration of the sensor technology highlighted the random behavior of water-soil dynamics despite the small-scale scope of the experiment. The results showed the potential of this methodology to assess watering needs and adjust watering resources to the vegetation status using real-time atmospheric and soil datainfo:eu-repo/semantics/publishedVersio

Social Equity Matters in Payments for Ecosystem Services

Although conservation efforts have sometimes succeeded in meeting environmental goals at the expense of equity considerations, the changing context of conservation and a growing body of evidence increasingly suggest that equity considerations should be integrated into conservation planning and implementation. However, this approach is often perceived to be at odds with the prevailing focus on economic efficiency that characterizes many payment for ecosystem services (PES) schemes. Drawing from examples across the literature, we show how the equity impacts of PES can create positive and negative feedbacks that influence ecological outcomes. We caution against equity-blind PES, which overlooks these relationships as a result of a primary and narrow focus on economic efficiency. We call for further analysis and better engagement between the social and ecological science communities to understand the relationships and trade-offs among efficiency, equity, and ecological outcomes

A Review of Deep Learning Methods and Applications for Unmanned Aerial Vehicles

Deep learning is recently showing outstanding results for solving a wide variety of robotic tasks in the areas of perception, planning, localization, and control. Its excellent capabilities for learning representations from the complex data acquired in real environments make it extremely suitable for many kinds of autonomous robotic applications. In parallel, Unmanned Aerial Vehicles (UAVs) are currently being extensively applied for several types of civilian tasks in applications going from security, surveillance, and disaster rescue to parcel delivery or warehouse management. In this paper, a thorough review has been performed on recent reported uses and applications of deep learning for UAVs, including the most relevant developments as well as their performances and limitations. In addition, a detailed explanation of the main deep learning techniques is provided. We conclude with a description of the main challenges for the application of deep learning for UAV-based solutions

Governing for ecosystem health and human wellbeing

Governance arrangements and processes influence access to and benefits from ecosystem services, and therefore the potential for ecosystem services to alleviate poverty. Governance also then influences the health of ecosystems. This chapter learns from decades of governance-related research to identify how to make ecosystem governance more effectively ‘pro-poor’. It is informed by a systematic mapping of literature related to governance of ecosystem services and renewable natural resources for improved wellbeing and poverty alleviation, expert interviews and a workshop with government and non-government actors across a range of sectors from both North and South. The chapter is organised around the concept of trade-offs, considering first ecosystem-focused approaches, then rights-based approaches and lastly, participatory approaches to governance. The chapter further addresses the relevance of scale and multiple administrative levels (multi-level governance) and the importance of informal, or socially embedded, institutions. The chapter concludes that there is no single governance approach that can definitively deliver on improved ecosystem health and human wellbeing, that trade-offs are inevitable and governance is therefore an inherently political process

Attitude estimation using horizon detection in thermal images

The lack of redundant attitude sensors represents a considerable yet common vulnerability in many low-cost unmanned aerial vehicles. In addition to the use of attitude sensors, exploiting the horizon as a visual reference for attitude control is part of human pilots' training. For this reason, and given the desirable properties of image sensors, quite a lot of research has been conducted proposing the use of vision sensors for horizon detection in order to obtain redundant attitude estimation onboard unmanned aerial vehicles. However, atmospheric and illumination conditions may hinder the operability of visible light image sensors, or even make their use impractical, such as during the night. Thermal infrared image sensors have a much wider range of operation conditions and their price has greatly decreased during the last years, becoming an alternative to visible spectrum sensors in certain operation scenarios. In this paper, two attitude estimation methods are proposed. The first method consists of a novel approach to estimate the line that best fits the horizon in a thermal image. The resulting line is then used to estimate the pitch and roll angles using an infinite horizon line model. The second method uses deep learning to predict attitude angles using raw pixel intensities from a thermal image. For this, a novel Convolutional Neural Network architecture has been trained using measurements from an inertial navigation system. Both methods presented are proven to be valid for redundant attitude estimation, providing RMS errors below 1.7° and running at up to 48 Hz, depending on the chosen method, the input image resolution and the available computational capabilities

ラッピング数、充足性と随伴結び目の最小交点数について

学位の種別: 課程博士審査委員会委員 : （主査）東京大学教授 河野 俊丈, 東京大学教授 坪井 俊, 東京大学教授 古田 幹雄, 東京大学准教授 河澄 響矢, 東京大学准教授 逆井 卓也, 東京大学准教授 北山 貴裕University of Tokyo(東京大学

Human interleukin-13 activates the interleukin-4-dependent transcription factor NF-IL4 sharing a DNA binding motif with an interferon-γ-induced nuclear binding factor

AbstractThe effects of interleukin-13 (IL-13) and interleukin-4 (IL-4) on cellular functions were shown to be quite similar. We provide evidence that in monocytes as well as in T lymphocytes both IL-4 and IL- 13 activate the same recently identified transcription factor NF-IL4 which binds to the specific responsive element IL-4RE. In addition, we show that a nuclear factor activated by interferon-γ also interacts with the IL-4RE. It differs from NF-IL4 in the electrophoretic mobility of the complex with DNA, in its DNA-binding specificity and in the proteins interacting with the DNA sequence. Sensitivity against various enzyme inhibitors suggests that components of the signal transduction pathway are shared by all three cytokines

Os efectos das infraestruturas humanas sobre a fauna galega de interese veterinario

Nesta revisión exponse, de xeito resumido cales son os efectos das infraestruturas desenvolvidas polo home en Galiza (terra, mar e aire) sobre o benestar animal de diversas especies de interese veterinario. Estre traballo explora ademáis a eficacia de certas medidas para controlar os efectos negativos e as repercusións no medio ambiente de ditas infraestruturas

Functional Enrichment Analysis of Regulatory Elements

This work has been partially supported by FEDER/Junta de Andalucia-Consejeria de Economia y Conocimiento/(grant CV20-36723), grant PID2020-119032RB-I00, MCIN/AEI/10.13039/501100011033 and FEDER/Junta de Andalucia-Consejeria de Transformacion Economica, Industria, Conocimiento y Universidades (Grant P20_00335).Statistical methods for enrichment analysis are important tools to extract biological information from omics experiments. Although these methods have been widely used for the analysis of gene and protein lists, the development of high-throughput technologies for regulatory elements demands dedicated statistical and bioinformatics tools. Here, we present a set of enrichment analysis methods for regulatory elements, including CpG sites, miRNAs, and transcription factors. Statistical significance is determined via a power weighting function for target genes and tested by theWallenius noncentral hypergeometric distribution model to avoid selection bias. These new methodologies have been applied to the analysis of a set of miRNAs associated with arrhythmia, showing the potential of this tool to extract biological information from a list of regulatory elements. These new methods are available in GeneCodis 4, a web tool able to perform singular and modular enrichment analysis that allows the integration of heterogeneous information.FEDER/Junta de Andalucia-Consejeria de Economia y Conocimiento CV20-36723MCIN/AEI PID2020-119032RB-I00FEDER/Junta de Andalucia-Consejeria de Transformacion Economica, Industria, Conocimiento y Universidades P20_0033