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[EN] Elevated atmospheric nitrogen (N) deposition on terrestrial ecosystems has become one of the most important drivers of microbial diversity loss on a global scale, and has been reported to alter the soil function of nutrient-poor, montane Calluna vulgaris heathlands in the context of global change. In this work we analyze for the first time the shifts of bacterial communities in response to experimental addition of N in Calluna heathlands as a simulation of atmospheric deposition. Specifically, we evaluated the effects of five N addition treatments (0, 10, 20, and 50 kg N ha−1 yr−1 for 3-years; and 56 kg N ha−1 yr−1 for 10-years) on the resistance of soil bacterial communities as determined by changes in their composition and alpha and beta diversities. The study was conducted in montane Calluna heathlands at different development stages (young and mature phases) in the southern side of the Cantabrian Mountains (NW Spain). Our results evidenced a substantial increase of long-term (10-years) N inputs on soil extractable N-NH4+, particularly in young Calluna stands. The alpha diversity of soil bacterial communities in mature Calluna stands did not show a significant response to experimental N addition, whereas it was significantly higher under long-term chronic N addition (56 kg N ha−1 yr−1 for 10-years) in young Calluna stands. These bacterial community shifts are mainly attributable to a decrease in the dominance of Acidobacteria phylum, the most representative in montane Calluna ecosystems, in favor of copiotrophic taxa such as Actinobacteria or Proteobacteria phyla, favored under increased N availability. Future research should investigate what specific ecosystem functions performed by soil bacterial communities may be sensitive to increased nitrogen depositions, which may have substantial implications for the understanding of montane Calluna ecosystems' stability. SI Next-Generation Funds of the European Union (EU) in the framework of the FIREMAP project (TED2021-130925B-I00) Regional Government of Castile and León in the framework of the IA-FIREXTCyL project (LE081P23) Portuguese Foundation for Science and Technology in the frame of project UIDB/04033/2020 Spanish Ministry of Universities (FPU21/00309)

User interfaces for digital twins (DTs) should provide information that allows the user to be aware of the state of the physical entity that is virtualised. Typically, this real entity is a shared space in which various human and artificial agents interact (for instance, in smart cities, various citizens and vehicles interact; in manufacturing, operators and machinery cooperate in the production, etc.) and the user interface must provide information about its state. This work presents ADD (Awareness Description Diagrams) as a technique for modelling requirements of Human-DT interaction. A study was conducted in order to virtualise a natural space where groups of hikers carry out their activities. This study assesses the learning curve of ADD, its feasibility to model Human-DT requirements, and its utility to design user interfaces. The results of the study provide valuable insights into the effectiveness of the ADD technique. This work is funded in part by the Dirección General Universidades, Investigación e Innovación, Spain, Junta de Comunidades de CastillaLa Mancha, Spain, and the European Regional Development Fund (ERDF), grant number SBPLY/21/180501/000244. This work has been supported by grant "Redes de Interconexion, Aceleradores Hardware 𝑦 Optimizacion de Aplicaciones" (PID2019-105660RB-C22, funded by MCIN/ AEI /10.13039/501100011033).

Nowadays, companies collect massive quantities of data to enhance their operations, often at the expense of sharing user sensible information. This data is widely used to train Deep Learning (DL) neural networks to model, classify, or recognize complex data. These activities enable companies to offer an array of services to users, such as precise advertising and optimal location services. This study explores potential solutions for preserving privacy while utilizing DL applications. To address the privacy issue, we develop a privacy-preserving framework specifically designed for fog computing environments. Unlike traditional cloud computing architectures, fog embedded architectures only share a small portion of user data with a nearby fog node, ensuring that the majority of sensitive data remains secure. Within these fog nodes, we incorporate two additional algorithms, namely Generalization and Threshold, to enhance the privacy-preserving capabilities of the framework. The first algorithm, Generalization, introduces a validation dataset within the fog nodes which not only increases the accuracy of the fog-embedded framework but also ensures that user data is preserved. The second algorithm, Threshold, is responsible for protecting user data samples and reducing the amount of information sent to the server. By combining these two algorithms, we are able to provide an additional layer of protection for user privacy while still maintaining the accuracy of the model. We conduct an evaluation to test its effectiveness using two separate datasets. In addition, we analyze them through a Feed Forward Neural Network (FFNN) and compare the results with a traditional centralized architecture to validate the effectiveness of the proposed framework. The results of our evaluation demonstrate that the proposed privacy-preserving framework, when combined with the Generalization and Threshold algorithms, can preserve up to 38.44% of user data. Additionally, we were able to extend the framework to multiple fog nodes without compromising the network’s accuracy, as we only observed a 0.1% decrease in accuracy when using the proposed architecture. This study emphasizes the importance of preserving user information while using DL applications and provides a solution that trains the desired network without violating user privacy, hence preserving their anonymity. Overall, the study highlights the potential of Federated Deep Learning to improve the accuracy and privacy of DL applications in fog computing environments. This work is partially supported by the Spanish Ministry of Science and Innovation, Spain under contracts PID2021-124463OB-IOO and PID2019-107255GB-C22; by the Generalitat de Catalunya, Spain under grant 2021SGR00326; and by the DRAC (IU16-011591), the HORIZON Vitamin-V, Spain(101093062), the HORIZON-AG PHOENI2X, Spain (101070586) and the HORIZON HORSE, Spain (101096342) projects. Peer Reviewed

The inclusion of IoT in digital platforms is very common nowadays due to the ease of deployment, low power consumption and low cost. It is also common to use heterogeneous IoT devices of ad-hoc or commercial development, using private or third-party network infrastructures. This scenario makes it difficult to detect invalid packets from malfunctioning devices, from sensors to application servers. These invalid packets generate low quality or erroneous data, which negatively influence the services that use them. For this reason, we need to create procedures and mechanisms to ensure the quality of the data obtained from IoT infrastructures, regardless of the type of infrastructure and the control we have over them, so that the systems that use this data can be reliable. In this work we propose the development of an Anomaly Detection System for IoT infrastructures based on Machine Learning using unsupervised learning. We validate the proposal by implementing it on the IoT infrastructure of the University of Alicante, which has a multiple sensing system and uses third-party services, over a campus of one million square meters. The contribution of this work has been the generation of an anomaly detection system capable of revealing incidents in IoT infrastructures, without knowing details about the infrastructures or devices, through the analysis of data in real time. This proposal allows to discard from the IoT data flow all those packets that are suspected to be anomalous to ensure a high quality of information to the tools that consume IoT data. This project has been funded by the UAIND22-01B project "Adaptive control of urban supply systems" of the University of Alicante.

Background Mobile health systems integrating wearable devices are emerging as promising tools for registering pain-related factors. However, their application in populations with chronic conditions has been underexplored. Objective To design a semi-automatic mobile health system with wearable devices for evaluating the potential predictive relationship of pain qualities and thresholds with heart rate variability, skin conductance, perceived stress, and stress vulnerability in individuals with preclinical chronic pain conditions such as suspected rheumatic disease. Methods A multicenter, observational, cross-sectional study was conducted with 67 elderly participants. Predicted variables were pain qualities and pain thresholds, assessed with the McGill Pain Questionnaire and a pressure algometer, respectively. Predictor variables were heart rate variability, skin conductance, perceived stress, and stress vulnerability. Multiple linear regression analyses were conducted to examine the influence of the predictor variables on the pain dimensions. Results The multiple linear regression analysis revealed that the predictor variables significantly accounted for 27% of the variability in the affective domain, 14% in the miscellaneous domain, 15% in the total pain rating index, 10% in the number of words chosen, 14% in the present pain intensity, and 16% in the Visual Analog Scale scores. Conclusion The study found significant predictive values of heart rate variability, skin conductance, perceived stress, and stress vulnerability in relation to pain qualities and thresholds in the elderly population with suspected rheumatic disease. The comprehensive integration of physiological and psychological stress measures into pain assessment of elderly individuals with preclinical chronic pain conditions could be promising for developing new preventive strategies. R&D&i Project Ref. PID2019-109644RB-I00 funded by the Ministerio de Ciencia e Innovación / Agencia Estatal de Investigación / 10.13039/501100011033 R&D&i Project Ref. B-TIC-320-UGR20 funded by Junta de Andalucía and “ERDF A way of making Europe.”

The Digital Transformation of firms is in an incipient stage despite being essential for competitiveness. Hence, the objective of the present study is to analyse which factors are important in the realisation of an Organisation 4.0. To this end we revise the literature to analyse the concept of Industry 4.0, identifying which technologies are necessary for this fourth industrial revolution, and the process of becoming an Organisation 4.0. A model is then proposed with the antecedent variables required for an Organisation 4.0, which include the roles of innovation, business intelligence and digital employees. The model is tested through a sample of 198 firms using the structural equations technique. Although technologies 4.0 have usually been considered as facilitators of innovation, the present study finds the opposite relationship, i.e. that innovation is a necessary antecedence for Organisation 4.0. It is also found that this stimulus is reinforced by Business Intelligence systems and that innovation strengthens the presence of digital employees. However, we have not been able to show how these digital employees contribute to the formation of an Organisation 4.0. More research is needed on the employee knowledge, skills and capabilities required for the implementation of an Organisation 4.0.

Este artículo científico aborda el impacto del calentamiento global en las Reservas de la Biosfera de alta montaña en España, centrándose en el periodo de estudio entre 2001 y 2016. Mediante el análisis de las tendencias del Índice de Vegetación de Diferencia Normalizada (NDVI), se evalúa la salud ecológica y los cambios ambientales en dos reservas específicas: Ordesa-Viñamala y Sierra Nevada. El estudio revela que, a pesar de una tendencia general positiva en el NDVI cuando se consideran ambas reservas juntas, existen diferencias significativas entre ellas. La Reserva Ordesa-Viñamala muestra una tendencia más positiva en el NDVI, indicando una menor vulnerabilidad a los efectos negativos del cambio climático, en contraste con la Reserva Sierra Nevada, que experimenta impactos negativos más pronunciados, reflejados en tendencias de NDVI menos favorables. Este análisis destaca cómo los cambios en variables ambientales como la temperatura y la sequía, exacerbados por el cambio climático, están afectando diferencialmente a estas áreas protegidas. Mientras que la reserva alpina (Ordesa-Viñamala) mantiene una resiliencia relativamente alta, la reserva mediterránea (Sierra Nevada) sufre de manera más aguda las consecuencias del cambio climático. El estudio subraya la importancia de las Reservas de la Biosfera como herramientas cruciales para la conservación de la biodiversidad y el fomento del desarrollo sostenible, a la vez que enfatiza la necesidad de una evaluación continua de estas áreas para ajustar las estrategias de manejo y conservación. Además, resalta el valor del trabajo comunitario local en la gestión de estos sistemas socioecológicos, proporcionando evidencia significativa de que las Reservas de la Biosfera son efectivas en la comprensión y gestión sostenible de los ecosistemas frente a desafíos ambientales complejos. El calentamiento global está aumentando la vulnerabilidad de los ecosistemas, especialmente en la España peninsular. Las Reservas de la Biosfera son áreas protegidas internacionalmente que buscan proteger la biodiversidad y, al mismo tiempo, promover el desarrollo sostenible. Evaluar estas áreas protegidas es esencial para verificar los cambios ambientales y establecer prioridades en su gestión. En este trabajo, hemos estudiado las tendencias temporales del NDVI en las Reservas de la Biosfera de alta montaña de España desde 2001 hasta 2016 para comprobar si los patrones de tendencia están asociados con algunas variables ambientales. Se encontraron diferencias significativas entre las tendencias del NDVI y las Reservas de la Biosfera de alta montaña. En primer lugar, se observaron tendencias positivas significativas en el NDVI al analizar ambas reservas juntas. Sin embargo, se encontraron diferencias significativas entre las dos reservas. La Reserva de Ordesa-Viñamala muestra tendencias positivas más altas del NDVI y tendencias negativas más bajas, mientras que este patrón se invierte en Sierra Nevada. Observamos cómo las fluctuaciones en la temperatura y la sequía debido al cambio climático ya han afectado negativamente a la reserva mediterránea (Sierra Nevada). En contraste, la reserva alpina (Ordesa-Viñamala) mantiene tendencias positivas del NDVI. Este estudio ayuda a cerrar la brecha en la información relacionada con las Reservas de la Biosfera, lo que da valor al trabajo que llevan a cabo las comunidades locales que las componen, generando resultados estadísticamente significativos que las Reservas de la Biosfera son áreas protegidas que nos ayudan a entender cómo gestionar y gobernar los sistemas socioecológicos de manera sostenible.

Human resource management (HRM) is a crucial aspect of the global economy, and there is a wealth of literature available on various aspects of managing human resources. There is a need to take these concepts and turn them into practical applications, and organizations and academic institutions have a vital role to play. By providing training and digital tools to enhance innovation and decision making, these entities can prepare the next generation of human resources and business leaders for the challenges they may face. This is especially relevant in light of the economic impact of pandemics and other unpredictable global events, which can have long-lasting effects on the economy. To address these challenges, a study was conducted to explore the potential use of business game simulators (BGS) as a solution. The results of the study are promising, showing that BGS can enhance pandemic preparedness, increase competitiveness, and provide a more comprehensive organizational viewpoint. To explore this hypothesis, the study used specific constructs, which were subjected to empirical processing and analysis. The results indicate that simulating past pandemics through BGS can help HRM and businesses be better prepared for future crises, and the BGS learning approach can offer a more realistic, global perspective for organizations

EMIT is supported by the National Aeronautics and Space Administration Earth Venture Instrument program, under the Earth Science Division of the Science Mission Directorate. K. Dana Chadwick is supported by the NASA Applied Sciences Program. Carlos P´ erez García- Pando and María Gonçalves Ageitos acknowledge support from the European Research Council (ERC) Consolidator Grant FRAGMENT (grant agreement No. 773051), and the AXA Chair on Sand and Dust Storms at the Barcelona Supercomputing Center funded by the AXA Research Fund. Martina Klose has received funding through the Helmholtz Association’s Initiative and Networking Fund (grant agreement No. VH-NG-1533). We thank Jeffrey Czapla-Myers and the University of Arizona team for their maintenance and operation of the Railroad Valley automated calibration facility. This research was performed at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. We acknowledge the support and assistance of NASA’s International Space Station Program. The USGS authors’ contribution to this published Work was prepared by U.S. federal government employees as part of their official duties and constitutes a “work of the United States government,” and is considered to be in the public domain and therefore domestic copyright does not apply. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. Copyright 2024 California Institute of Technology. All rights reserved. US Government Support Acknowledged. The Earth surface Mineral dust source InvesTigation (EMIT) is a remote visible to shortwave infrared (VSWIR) imaging spectrometer that has been operating onboard the International Space Station since July 2022. This article describes EMIT's on-orbit spectroradiometric calibration and validation. Accurate spectroscopy is vital to achieve consistent mapping results with orbital imaging spectrometers. EMIT takes a unique approach to this challenge, with just six optical elements, no shutter, and no onboard calibration systems. Its simple design focuses on uniformity and stability to enable vicarious spectroradiometric calibration. Our experiments demonstrate that this approach is successful, approaching the fidelity of manual field spectroscopy in some cases, and enabling new and more accurate products across diverse Earth science disciplines. EMIT achieves several notable firsts for an instrument of its class. It demonstrates successful on-orbit adjustments of Focal Plane Array (FPA) alignment with sub-micron precision. It offers spectral uniformity better than 98%. Optical artifacts in the measurement channels are at least three orders of magnitude below the primary solar-reflected surface signals. Its noise performance enables percent-level discrimination in the depths of mineral absorption features. In these aspects, EMIT satisfies the stringent performance needs for the next generation of VSWIR imaging spectrometers to observe the Earth's ecosystems, geology, and water resources. Article signat per 56 autors: David R. Thompson, Robert O. Green, Christine Bradley, Philip G. Brodrick, Natalie Mahowald, Eyal Ben Dor, Matthew Bennett, Michael Bernas, Nimrod Carmon, K. Dana Chadwick, Roger N. Clark, Red Willow Coleman, Evan Cox, Ernesto Diaz, Michael L. Eastwood, Regina Eckert, Bethany L. Ehlmann, Paul Ginoux, María Gonçalves Ageitos, Kathleen Grant, Luis Guanter, Daniela Heller Pearlshtien, Mark Helmlinger, Harrison Herzog, Todd Hoefen, Yue Huang, Abigail Keebler, Olga Kalashnikova, Didier Keymeulen, Raymond Kokaly, Martina Klose, Longlei Li, Sarah R. Lundeen, John Meyer, Elizabeth Middleton, Ron L. Miller, Pantazis Mouroulis, Bogdan Oaida, Vincenzo Obiso, Francisco Ochoa, Winston Olson-Duvall, Gregory S. Okin, Thomas H. Painter, Carlos Pérez García-Pando, Randy Pollock, Vincent Realmuto, Lucas Shaw, Peter Sullivan, Gregg Swayze, Erik Thingvold, Andrew K. Thorpe, Suresh Vannan, Catalina Villarreal, Charlene Ung, Daniel W. Wilson, Sander Zandbergen. Objectius de Desenvolupament Sostenible::13 - Acció per al Clima Peer Reviewed