Quantification of prominent volatile compounds responsible for muskmelon and watermelon aroma by purge and trap extraction followed by gas chromatography–mass spectrometry determination

A dynamic headspace purge-and-trap (DHS-P&T) methodology for the determination and quantification of 61 volatile compounds responsible for muskmelon and watermelon aroma has been developed and validated. The methodology is based on the application of purge-and-trap extraction followed by gas chromatography coupled to (ion trap) mass spectrometry detection. For this purpose two different P&T sorbent cartridges have been evaluated. The influence of different extraction factors (sample weight, extraction time, and purge flow) on extraction efficiency has been studied and optimised using response surface methodology. Precision, expressed as repeatability, has been evaluated by analysing six replicates of real samples, showing relative standard deviations between 3% and 27%. Linearity has been studied in the range of 10–6130 ng mL−1 depending on the compound response, showing coefficients of correlation between 0.995 and 0.999. Detection limits ranged between 0.1 and 274 ng g−1. The methodology developed is well suited for analysis of large numbers of muskmelon and watermelon samples in plant breeding programs

GPU Parallel Implementation of Dual-Depth Sparse Probabilistic Latent Semantic Analysis for Hyperspectral Unmixing

Hyperspectral unmixing (HU) is an important task for remotely sensed hyperspectral (HS) data exploitation. It comprises the identification of pure spectral signatures (endmembers) and their corresponding fractional abundances in each pixel of the HS data cube. Several methods have been developed for (semi-) supervised and automatic identification of endmembers and abundances. Recently, the statistical dual-depth sparse probabilistic latent semantic analysis (DEpLSA) method has been developed to tackle the HU problem as a latent topic-based approach in which both endmembers and abundances can be simultaneously estimated according to the semantics encapsulated by the latent topic space. However, statistical models usually lead to computationally demanding algorithms and the computational time of the DEpLSA is often too high for practical use, in particular, when the dimensionality of the HS data cube is large. In order to mitigate this limitation, this article resorts to graphical processing units (GPUs) to provide a new parallel version of the DEpLSA, developed using the NVidia compute device unified architecture. Our experimental results, conducted using four well-known HS datasets and two different GPU architectures (GTX 1080 and Tesla P100), show that our parallel versions of the DEpLSA and the traditional pLSA approach can provide accurate HU results fast enough for practical use, accelerating the corresponding serial versions in at least 30x in the GTX 1080 and up to 147x in the Tesla P100 GPU, which are quite significant acceleration factors that increase with the image size, thus allowing for the possibility of the fast processing of massive HS data repositories

A New Deep Generative Network for Unsupervised Remote Sensing Single-Image Super-Resolution

Super-resolution (SR) brings an excellent opportunity to improve a wide range of different remote sensing applications. SR techniques are concerned about increasing the image resolution while providing finer spatial details than those captured by the original acquisition instrument. Therefore, SR techniques are particularly useful to cope with the increasing demand remote sensing imaging applications requiring fine spatial resolution. Even though different machine learning paradigms have been successfully applied in SR, more research is required to improve the SR process without the need of external high-resolution (HR) training examples. This paper proposes a new convolutional generator model to super-resolve low-resolution (LR) remote sensing data from an unsupervised perspective. That is, the proposed generative network is able to initially learn relationships between the LR and HR domains throughout several convolutional, downsampling, batch normalization, and activation layers. Then, the data are symmetrically projected to the target resolution while guaranteeing a reconstruction constraint over the LR input image. An experimental comparison is conducted using 12 different unsupervised SR methods over different test images. Our experiments reveal the potential of the proposed approach to improve the resolution of remote sensing imagery

Multimodal Probabilistic Latent Semantic Analysis for Sentinel-1 and Sentinel-2 Image Fusion

Probabilistic topic models have recently shown a great potential in the remote sensing image fusion field, which is particularly helpful in land-cover categorization tasks. This letter first studies the application of probabilistic latent semantic analysis (pLSA) and latent Dirichlet allocation to remote sensing synthetic aperture radar (SAR) and multispectral imaging (MSI) unsupervised land-cover categorization. Then, a novel pLSA-based image fusion approach is presented, which pursues to uncover multimodal feature patterns from SAR and MSI data in order to effectively fuse and categorize Sentinel-1 and Sentinel-2 remotely sensed data. Experiments conducted over two different data sets reveal the advantages of the proposed approach for unsupervised land-cover categorization tasks

Depresión y conductas autolesivas en adolescentes de Lima Metropolitana, 2023

Este trabajo tuvo como objetivo analizar la relación entre la depresión y las conductas autolesivas, para ello se reunió una muestra de 355 adolescentes de edades entre 12 a 18 años (M=14.95, DE=1.519), 41.7% mujeres. El estudio fue no experimental, transversal y correlacional. Se aplicaron la Escala de Depresión de Zung y el Cédula de Conductas Autolesivas. El resultado identificó relación directa, moderada y significativa entre las variables (r=.349, p<.001), de tamaño de efecto pequeño (r2=.121), de igual forma se hallaron relación directas entre las conductas autolesivas y las dimensiones de la depresión (r=.374, .190, .313 y .275, p<.001) y entre la depresión con los componentes de las conductas autolesivas (r=.301 y .360, p<.001), y de tamaño de efectos pequeños en todos los casos. Finalmente, los análisis comparativos permitieron identificar que tanto para la depresión y conductas autolesivas junto a sus dimensiones existen diferencias significativas en función del sexo, siendo las mujeres quienes presentaron un rango promedio mayor que los hombres, mientras que no se identificaron diferencias al contrastar los grupos de edades. Se concluye que el incremento de la depresión se asocia con el aumento de conductas autolesivas y quienes más la padecen son las mujeres

Remote Sensing Single-Image Superresolution Based on a Deep Compendium Model

This letter introduces a novel remote sensing single-image superresolution (SR) architecture based on a deep efficient compendium model. The current deep learning-based SR trend stands for using deeper networks to improve the performance. However, this practice often results in the degradation of visual results. To address this issue, the proposed approach harmonizes several different improvements on the network design to achieve state-of-the-art performance when superresolving remote sensing imagery. On the one hand, the proposal combines residual units and skip connections to extract more informative features on both local and global image areas. On the other hand, it makes use of parallelized 1x1 convolutional filters (network in network) to reconstruct the superresolved result while reducing the information loss through the network. Our experiments, conducted using seven different SR methods over the well-known UC Merced remote sensing data set, and two additional GaoFen-2 test images, show that the proposed model is able to provide competitive advantages