Quantum coherence and the principle of microscopic reversibility

The principle of microscopic reversibility is a fundamental element in the formulation of fluctuation relations and the Onsager reciprocal relations. As such, a clear description of whether and how this principle is adapted to the quantum mechanical scenario might be essential to a better understanding of nonequilibrium quantum processes. Here, we propose a quantum generalization of this principle, which highlights the role played by coherence in the symmetry relations involving the probability of observing a quantum transition and that of the corresponding time reversed process. We study the implications of our findings in the framework of a qubit system interacting with a thermal reservoir, and implement an optical experiment that simulates the dynamics. Our theoretical and experimental results show that the influence of coherence is more decisive at low temperatures and that the maximum departure from the classical case does not take place for maximally coherent states. Classical predictions are recovered in the appropriate limits

An endemic‐rich island through the eyes of children: Wildlife identification and conservation preferences in São Tomé (Gulf of Guinea)

Species that the public knows and is willing to protect often do not align with international conservation priorities. Assessing perceptions on wildlife is thus essential to guide conservation initiatives, especially in island developing states where native and introduced species often have contrasting values for biodiversity. We used a game to assess the ability of third class students in São Tomé Island (São Tomé and Príncipe, central Africa) to identify wildlife and their conservation preferences. Students correctly identified 28% of the animals shown. Children who were poorer, male or from rural schools were more likely to correctly identify species. Urban children were less successful identifying species endemic to São Tomé and Príncipe than rural children. Conservation preferences were not associated with species identification and instead were justified by subjective species-specific traits, such as attractiveness or profitability. Despite the low identification rates for endemic (10% correct identifications) and threatened birds (2%), children were keen on preserving endemic species, indicating that these might become effective flagships for the unique biodiversity of the island. These results illustrate the need to consider separately the attributes that affect knowledge and willingness to protect, and how both can be used to guide conservation strategies.info:eu-repo/semantics/publishedVersio

Detection of quantum non-Markovianity close to the Born-Markov approximation

We calculate in an exact way the conditional past-future correlation for the decay dynamics of a two-level system in a bosonic bath. Different measurement processes are considered. In contrast to quantum memory measures based solely on system propagator properties, here memory effects are related to a convolution structure involving two system propagators and the environment correlation. This structure allows to detect memory effects even close to the validity of the Born-Markov approximation. An alternative operational-based definition of environment-to-system backflow of information follows from this result. We provide experimental support to our results by implementing the dynamics and measurements in a photonic experiment.Fil: Silva, Thais De Lima. Universidade Federal do Rio de Janeiro; BrasilFil: Walborn, Stephen P.. Universidade Federal do Rio de Janeiro; BrasilFil: Santos, Marcelo F.. Universidade Federal do Rio de Janeiro; BrasilFil: Aguilar, Gabriel H.. Universidade Federal do Rio de Janeiro; BrasilFil: Budini, Adrian Adolfo. Comisión Nacional de Energía Atómica. Centro Atómico Bariloche; Argentina. Universidad Tecnológica Nacional; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte; Argentin

Adaptive path planning for fusing rapidly exploring random trees and deep reinforcement learning in an agriculture dynamic environment UAVs

Unmanned aerial vehicles (UAV) are a suitable solution for monitoring growing cultures due to the possibility of covering a large area and the necessity of periodic monitoring. In inspection and monitoring tasks, the UAV must find an optimal or near-optimal collision-free route given initial and target positions. In this sense, path-planning strategies are crucial, especially online path planning that can represent the robot’s operational environment or for control purposes. Therefore, this paper proposes an online adaptive path-planning solution based on the fusion of rapidly exploring random trees (RRT) and deep reinforcement learning (DRL) algorithms applied to the generation and control of the UAV autonomous trajectory during an olive-growing fly traps inspection task. The main objective of this proposal is to provide a reliable route for the UAV to reach the inspection points in the tree space to capture an image of the trap autonomously, avoiding possible obstacles present in the environment. The proposed framework was tested in a simulated environment using Gazebo and ROS. The results showed that the proposed solution accomplished the trial for environments up to 300 m3 and with 10 dynamic objects.The authors would like to thank the following Brazilian Agencies CEFET-RJ, CAPES, CNPq, and FAPERJ. The authors also want to thank the Research Centre in Digitalization and Intelligent Robotics (CeDRI), Instituto Politécnico de Bragança–IPB (UIDB/05757/2020 and UIDP/05757/2020), the Foundation for Science and Technology (FCT, Portugal) for financial support through national funds FCT/MCTES (PIDDAC) to CeDRI, and Laboratório Associado para a Sustentabilidade e Tecnologia em Regiões de Montanha (SusTEC) and IPB, Portugal. This work was carried out under the Project “OleaChain: Competências para a sustentabilidade e inovação da cadeia de valor do olival tradicional no Norte Interior de Portugal” (NORTE-06-3559-FSE-000188), an operation to hire highly qualified human resources, funded by NORTE 2020 through the European Social Fund (ESF).info:eu-repo/semantics/publishedVersio

Mass spectrometry-based untargeted metabolomics approaches for comprehensive structural annotation of bioactive metabolites from bushy cashew (Anacardium humile) fruits

Funding Information: The authors acknowledge financial support from the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES), Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) for the institutional and financial support. Publisher Copyright: © 2023Peer reviewedPostprin

Cooperative heterogeneous robots for autonomous insects trap monitoring system in a precision agriculture scenario

The recent advances in precision agriculture are due to the emergence of modern robotics systems. For instance, unmanned aerial systems (UASs) give new possibilities that advance the solution of existing problems in this area in many different aspects. The reason is due to these platforms’ ability to perform activities at varying levels of complexity. Therefore, this research presents a multiple-cooperative robot solution for UAS and unmanned ground vehicle (UGV) systems for their joint inspection of olive grove inspect traps. This work evaluated the UAS and UGV vision-based navigation based on a yellow fly trap fixed in the trees to provide visual position data using the You Only Look Once (YOLO) algorithms. The experimental setup evaluated the fuzzy control algorithm applied to the UAS to make it reach the trap efficiently. Experimental tests were conducted in a realistic simulation environment using a robot operating system (ROS) and CoppeliaSim platforms to verify the methodology’s performance, and all tests considered specific real-world environmental conditions. A search and landing algorithm based on augmented reality tag (AR-Tag) visual processing was evaluated to allow for the return and landing of the UAS to the UGV base. The outcomes obtained in this work demonstrate the robustness and feasibility of the multiple-cooperative robot architecture for UGVs and UASs applied in the olive inspection scenario.The authors would like to thank the Foundation for Science and Technology (FCT, Portugal) for financial support through national funds FCT/MCTES (PIDDAC) to CeDRI (UIDB/05757/2020 and UIDP/05757/2020) and SusTEC (LA/P/0007/2021). In addition, the authors would like to thank the following Brazilian Agencies CEFET-RJ, CAPES, CNPq, and FAPERJ. In addition, the authors also want to thank the Research Centre in Digitalization and Intelligent Robotics (CeDRI), Instituto Politécnico de Braganca (IPB) - Campus de Santa Apolonia, Portugal, Laboratório Associado para a Sustentabilidade e Tecnologia em Regiões de Montanha (SusTEC), Portugal, INESC Technology and Science - Porto, Portugal and Universidade de Trás-os-Montes e Alto Douro - Vila Real, Portugal. This work was carried out under the Project “OleaChain: Competências para a sustentabilidade e inovação da cadeia de valor do olival tradicional no Norte Interior de Portugal” (NORTE-06-3559-FSE-000188), an operation used to hire highly qualified human resources, funded by NORTE 2020 through the European Social Fund (ESF).info:eu-repo/semantics/publishedVersio

Assessing the Effectiveness of Chemical Marker Extraction from Amazonian Plant Cupuassu (Theobroma grandiflorum) by PSI-HRMS/MS and LC-HRMS/MS.

Acknowledgments The authors acknowledge the institutional and financial support from the Brazilian Federal Agency for Support and Evaluation of Graduate Education (CAPES) and the Brazilian Research Council (CNPq). Funding This research received no external funding.Peer reviewedPublisher PD

Heterogeneous Multi-Robot Collaboration for Coverage Path Planning in Partially Known Dynamic Environments

This research presents a cooperation strategy for a heterogeneous group of robots that comprises two Unmanned Aerial Vehicles (UAVs) and one Unmanned Ground Vehicles (UGVs) to perform tasks in dynamic scenarios. This paper defines specific roles for the UAVs and UGV within the framework to address challenges like partially known terrains and dynamic obstacles. The UAVs are focused on aerial inspections and mapping, while UGV conducts ground-level inspections. In addition, the UAVs can return and land at the UGV base, in case of a low battery level, to perform hot swapping so as not to interrupt the inspection process. This research mainly emphasizes developing a robust Coverage Path Planning (CPP) algorithm that dynamically adapts paths to avoid collisions and ensure efficient coverage. The Wavefront algorithm was selected for the two-dimensional offline CPP. All robots must follow a predefined path generated by the offline CPP. The study also integrates advanced technologies like Neural Networks (NN) and Deep Reinforcement Learning (DRL) for adaptive path planning for both robots to enable real-time responses to dynamic obstacles. Extensive simulations using a Robot Operating System (ROS) and Gazebo platforms were conducted to validate the approach considering specific real-world situations, that is, an electrical substation, in order to demonstrate its functionality in addressing challenges in dynamic environments and advancing the field of autonomous robots.The authors also would like to thank their home Institute, CEFET/RJ, the federal Brazilian research agencies CAPES (code 001) and CNPq, and the Rio de Janeiro research agency, FAPERJ, for supporting this work.info:eu-repo/semantics/publishedVersio