Water vapor pressure deficit in Portugal and implications for the development of the invasive African citrus psyllid trioza erytreae

African citrus psyllid (Trioza erytreae (Del Guercio)) is a vector insect of the bacterium Candidatus Liberibacter africanus, the putative causal agent of Huanglongbing, the most devastating citrus disease in the world. The insect was found on the island of Madeira in 1994 and in mainland Portugal in 2015. Present in the north and center of the country, it is a threat to Algarve, the main citrus-producing region. Trioza erytreae eggs and first instar nymphs are sensitive to the combination of high temperatures and low relative humidity. Daily maximum air temperature and minimum relative humidity data from 18 weather stations were used to calculate the water vapor pressure deficit (vpd) from 2004 to 2018 at various locations. Based on the mean vpd and the number of unfavorable days (vpd < 34.5 and vpd < 56 mbar) of two time periods (February to May and June to September), less favorable zones for T. erytreae were identified. The zones with thermal and water conditions like those observed in the Castelo Branco and Portalegre (Center), Beja (Alentejo), Alte, and Norinha (Algarve) stations showed climatic restrictions to the development of eggs and first instar nymphs of African citrus psyllid. Effective control measures, such as the introduction and mass release of Tamarixia dryi (Waterson), a specific parasitoid, and chemical control are necessary in favorable periods for T. erytreae development, such as in spring and in areas with limited or no climate restrictions.info:eu-repo/semantics/publishedVersio

Consumer Attitudes toward News Delivering: An Experimental Evaluation of the Use and Efficacy of Personalized Recommendations

This paper presents an experiment on newsreaders’ behavior and preferences on the interaction with online personalized news. Different recommendation approaches, based on consumption profiles and user location, and the impact of personalized news on several aspects of consumer decision-making are examined on a group of volunteers. Results show a significant preference for reading recommended news over other news presented on the screen, regardless of the chosen editorial layout. In addition, the study also provides support for the creation of profiles taking into consideration the evolution of user’s interests. The proposed solution is valid for users with different reading habits and can be successfully applied even to users with small consumption history. Our findings can be used by news providers to improve online services, thus increasing readers’ perceived satisfaction.Paula Viana and Márcio Soares were partial supported by Project “TEC4Growth—Pervasive Intelligence, Enhancers and Proofs of Concept with Industrial Impact/NORTE-01-0145-FEDER-000020”, under Research Line FourEyes, financed by the North Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, and through the European Regional Development Fund (ERDF). Paula Viana has also been supported by National Funds through the Portuguese funding agency, FCT—Fundação para a Ciência e a Tecnologia, within project UIDB/50014/2020. Rita Gaio was partially supported by CMUP, which is Financed by national funds through FCT—Fundação para a Ciência e a Tecnologia, I.P., under the project with the reference UIDB/00144/2020. Amílcar Correia was partially supported by the Project Pglobal (Nr. 2014/38592-Programa Operacional Temático Factores de Competitividade/Programa Operacional do Norte, Funded by ERDF).info:eu-repo/semantics/publishedVersio

Updating mining reserves with uncertainty data

In mining operations, the time delay between grade estimations and decision about the scheduling of stopes mining can result in seriously outdated information and, consequently, a substantial mined reserves bias. To mitigate this gap between the grade estimation of an orebody and its exploitation, this paper proposes a new method of speedily updating resources and reserves integrated into the concept of real-time mining. This consists in the continuous and swift update of mine reserves, which requires a continuous and fast stream of the measurements of stopes in an underground mine rather than the chemical lab analysis of core samples or chip/face samples. Here we propose using portable for the swift monitoring of ore grades. However, this “fast” data be highly uncertain. For this reason, the first step consists of creating a bidistribution function between “uncertain” XRF and the corresponding “hard” measurements, based on empirical historical data. Following this, the uncertainty of the XRF measurements is derived from those bi-distributions through the conditional distribution of real values given to the known XRF measurement.The second step involves updating the reserves by integrating this uncertain XRF data, which has been quantified by conditional distributions, in the grade characterization models. For this purpose, a stochastic simulation with point distributions is applied. A case study of a sulphide copper deposit illustrates the proposed methodology

Strategies for integrating uncertainty in iterative geostatistical seismic inversion

Iterative geostatistical seismic inversion integrates seismic and well data to infer the spatial distribution of subsurface elastic properties. These methods provide limited assessment to the spatial uncertainty of the inverted elastic properties, overlooking alternative sources of uncertainty such as those associated with poor well-log data, upscaling and noise within the seismic data. We express uncertain well-log samples, due to bad logging reads and upscaling, in terms of local probability distribution functions (PDFs). Local PDFs are used as conditioning data to a stochastic sequential simulation algorithm, included as the model perturbation within the inversion. The problem of noisy seismic and narrow exploration of the model parameter space, particularly at early steps of the inversion, is tackled by the introduction of a cap on local correlation coefficients responsible for the generation of the new set of models during the next iteration. A single geostatistical framework is proposed and illustrated with its application to a real case study. When compared against a conventional iterative geostatistical seismic inversion, the integration of additional sources of uncertainty increases the match between real and inverted seismic traces and the variability within the ensemble of models inverted at the last iteration. The selection of the local PDFs plays a central role in the reliability of the inverted elastic models. Avoiding high local correlation coefficients at early stages of the inversion increases convergence in terms of global correlation between synthetic and real seismic reflection data at the end of the inversion.Comment: 13 Figures

A Semi-Supervised Approach for the Semantic Segmentation of Trajectories

A first fundamental step in the process of analyzing movement data is trajectory segmentation, i.e., splitting trajecto- ries into homogeneous segments based on some criteria. Although trajectory segmentation has been the object of several approaches in the last decade, a proposal based on a semi-supervised approach remains inexistent. A semi-supervised approach means that a user labels manually a small set of trajectories with meaningful segments and, from this set, the method infers in an unsupervised way the segments of the remaining trajecto- ries. The main advantage of this method compared to pure supervised ones is that it reduces the human effort to label the number of trajectories. In this work, we propose the use of the Minimum Description Length (MDL) principle to measure homogeneity inside segments. We also introduce the Reactive Greedy Randomized Adaptive Search Procedure for semantic Semi- supervised Trajectory Segmentation (RGRASP-SemTS) algorithm that segments trajectories by combining a limited user labeling phase with a low number of input parameters and no predefined segmenting criteria. The approach and the algorithm are pre- sented in detail throughout the paper, and the experiments are carried out on two real-world datasets. The evaluation tests prove how our approach outperforms state-of-the-art competitors when compared to ground truth. This is a preprint version of the full article published by IEEE at https://ieeexplore.ieee.org/document/841127

Denoising Opponents Position in Partial Observation Environment

The RoboCup competitions hold various leagues, and the Soccer Simulation 2D League is a major among them. Soccer Simulation 2D (SS2D) match involves two teams, including 11 players and a coach for each team, competing against each other. The players can only communicate with the Soccer Simulation Server during the game. Several code bases are released publicly to simplify team development. So researchers can easily focus on decision-making and implementing machine learning methods. SS2D actions and behaviors are only partially accurate due to different challenges, such as noise and partial observation. Therefore, one strategy is to implement alternative denoising methods to tackle observation inaccuracy. Our idea is to predict opponent positions while they have yet to be seen in a finite number of cycles using machine learning methods to make more accurate actions such as pass. We will explain our position prediction idea powered by Long Short-Term Memory models (LSTM) and Deep Neural Networks (DNN). The results show that the LSTM and DNN predict the opponents' position more accurately than the standard algorithm, such as the last-seen method