Determination of oscillator strength of confined excitons in a semiconductor microcavity

We have achieved a significant experimental Rabi-splitting (3.4 meV) for confined polaritons in a planar semiconductor $\lambda$ microcavity for only a single quantum well (SQW) of GaAs (10 nm) placed at the antinode. The Rabi-splitting phenomena are discussed in detail based on the semiclassical theory, where two coupled harmonic oscillators (excitons and photons) are used to describe the system. In this way, we can obtain the dispersion curve of polaritons, the minimum value for the cavity reflectance and the oscillator strength to reach the strong coupling regime. This approach describes an ensemble of excitons confined in a SQW and includes a dissipation component. The results present a weak coupling regime, where an enhanced spontaneous emission takes place, and a strong coupling regime, where Rabi-splitting in the dispersion curve can be observed. The theoretical results are confronted with experimental data for the reflectance behavior in resonant and off-resonant conditions and present a great accuracy. This allows us to determine the oscillator strength of the confined excitons in the SQW with great precision.Comment: 11 pages, 7 figure

Nonequilibrium dynamics of the three-dimensional Edwards-Anderson spin-glass model with Gaussian couplings: Strong heterogeneities and the backbone picture

We numerically study the three-dimensional Edwards-Anderson model with Gaussian couplings, focusing on the heterogeneities arising in its nonequilibrium dynamics. Results are analyzed in terms of the backbone picture, which links strong dynamical heterogeneities to spatial heterogeneities emerging from the correlation of local rigidity of the bond network. Different two-times quantities as the flipping time distribution and the correlation and response functions, are evaluated over the full system and over high- and low-rigidity regions. We find that the nonequilibrium dynamics of the model is highly correlated to spatial heterogeneities. Also, we observe a similar physical behavior to that previously found in the Edwards-Anderson model with a bimodal (discrete) bond distribution. Namely, the backbone behaves as the main structure that supports the spin-glass phase, within which a sort of domain-growth process develops, while the complement remains in a paramagnetic phase, even below the critical temperature

Lattice Gauge Fixing for Parameter Dependent Covariant Gauges

We propose a non-perturbative procedure to fix generic covariant gauges on the lattice. Varying the gauge parameter, this gauge fixing provides a concrete method to check numerically the gauge dependence of correlators measured on the lattice. The new algorithm turns out to converge with a good efficiency. As a preliminary physical result, we find a sensitive dependence of the gluon propagator on the gauge parameter.Comment: 10 pages (LaTeX2e), 5 eps figure

Unmanned aerial vehicle and proximal sensing of vegetation indices in olive tree (Olea europaea)

Remote and proximal sensing platforms at the service of precision olive growing are bringing new development possibilities to the sector. A proximal sensing platform is close to the vegetation, while a remote sensing platform, such as unmanned aerial vehicle (UAV), is more distant but has the advantage of rapidity to investigate plots. The study aims to compare multispectral and hyperspectral data acquired with remote and proximal sensing platforms. The comparison between the two sensors aims at understanding the different responses their use can provide on a crop, such as olive trees having a complex canopy. The multispectral data were acquired with a DJI multispectral camera mounted on the UAV Phantom 4. Hyperspectral acquisitions were carried out with a FieldSpec® HandHeld 2™ Spectroradiometer in the canopy portions exposed to South, East, West, and North. The multispectral images were processed with Geographic Information System software to extrapolate spectral information for each cardinal direction’s exposure. The three main Vegetation indices were used: normalized difference vegetation index (NDVI), normalized difference red-edge index (NDRE), and modified soil adjusted vegetation index (MSAVI). Multispectral data e could describe the total variability of the whole plot differentiating each single plant status. Hyperspectral data were able to describe vegetation conditions more accurately; they appeared to be related to the cardinal exposure. MSAVI, NDVI, and NDRE showed correlation r =0.63**, 0.69**, and 0.74**, respectively, between multispectral and hyperspectral data. South and West exposures showed the best correlations with both platforms

Evaluation of Multispectral Data Acquired from UAV Platform in Olive Orchard

Precision agriculture is a management strategy to improve resource efficiency, production, quality, profitability and sustainability of the crops. In recent years, olive tree management is increasingly focused on determining the correct health status of the plants in order to distribute the main resource using different technologies. In the olive grove, the focus is often on the use of multispectral information from UAVs (Unmanned Aerial Vehicle), but it is not known how important spectral and biometric information actually is for the agronomic management of the olive grove. The aim of this study was to investigate the ability of multispectral data acquired from a UAV platform to predict nutritional status, biometric characteristics, vegetative condition and production of olive orchard as tool to DSS. Data were collected on vegetative characteristics closely related to vigour such as trunk cross-sectional area (TCSA), Nitrogen concentration of the leaves, canopy area and canopy volume. The production was collected for each plant to create an accurate yield map. The flight was carried out with a UAV equipped with a multispectral camera, at an altitude of 50 m and with RTK correction. The flight made it possible to determine the biometric condition and the spectral features through the normalized difference vegetation index (NDVI). The NDVI map allowed to determine the canopy area. The Structure for Motion (SfM) algorithm allow to determine the 3D canopy volume. The experiment showed that the NDVI was able to determine with high accuracy the vegetative characteristic as canopy area (r = 0.87 ***), TCSA (r = 0.58 ***) and production (r = 0.63 ***). The vegetative parameters are closely correlated with the production, especially the canopy area (r = 0.75 ***). Data clustering showed that the production of individual plants is closely dependent on leaf nitrogen concentration and vigour status

The Development of a Short Version of the SIMS Using Machine Learning to Detect Feigning in Forensic Assessment

In the present study, we applied machine learning techniques to evaluate whether the Structured Inventory of Malingered Symptomatology (SIMS) can be reduced in length yet maintain accurate discrimination between consistent participants (i.e., presumed truth tellers) and symptom producers. We applied machine learning item selection techniques on data from Mazza et al. (2019c) to identify the minimum number of original SIMS items that could accurately distinguish between consistent participants, symptom accentuators, and symptom producers in real personal injury cases. Subjects were personal injury claimants who had undergone forensic assessment, which is known to incentivize malingering and symptom accentuation. Item selection yielded short versions of the scale with as few as 8 items (to differentiate between consistent participants and symptom producers) and as many as 10 items (to differentiate between consistent and inconsistent participants). The scales had higher classification accuracy than the original SIMS and did not show the bias that was originally reported between false positives and false negatives

How to improve compliance with protective health measures during the covid-19 outbreak. Testing a moderated mediation model and machine learning algorithms

In the wake of the sudden spread of COVID-19, a large amount of the Italian population practiced incongruous behaviors with the protective health measures. The present study aimed at examining psychological and psychosocial variables that could predict behavioral compliance. An online survey was administered from 18–22 March 2020 to 2766 participants. Paired sample t-tests were run to compare efficacy perception with behavioral compliance. Mediation and moderated mediation models were constructed to explore the association between perceived efficacy and compliance, mediated by self-efficacy and moderated by risk perception and civic attitudes. Machine learning algorithms were trained to predict which individuals would be more likely to comply with protective measures. Results indicated significantly lower scores in behavioral compliance than efficacy perception. Risk perception and civic attitudes as moderators rendered the mediating effect of self-efficacy insignificant. Perceived efficacy on the adoption of recommended behaviors varied in accordance with risk perception and civic engagement. The 14 collected variables, entered as predictors in machine learning models, produced an ROC area in the range of 0.82–0.91 classifying individuals as high versus low compliance. Overall, these findings could be helpful in guiding age-tailored information/advertising campaigns in countries affected by COVID-19 and directing further research on behavioral compliance