The Microsoft KINECT: A novel tool for psycholinguistic research

The Microsoft KINECT is a 3D sensing device originally developed for the XBOX. The Microsoft KINECT opens up many exciting new opportunities for conducting experimental research on human behavior. We investigated some of these possibilities within the field of psycholinguistics (specifically: language production) by creating software, using C#, allowing for the KINECT to be used in a typical psycholinguistic experimental setting. The results of a naming experiment using this software confirmed that the KINECT was able to measure the effects of a robust psycholinguistic variable (word frequency) on naming latencies. However, although the current version of the software is able to measure psycholinguistic variables of interest, we also discuss several points where the software can still stand to be improved. The main aim of this paper is to make the software freely available for assessment and use by the psycholinguistic community and to illustrate the KINECT as a potentially valuable tool for investigating human behavior, especially in the field of psycholinguistics

Chapitre 2. Spatialiser les stocks de carbone

Introduction Dans les laboratoires d’analyse des sols à Madagascar, la mesure du carbone organique du sol (COS) sert à calculer la teneur en matière organique (MO), une information utile pour la gestion de la fertilité des sols. Outre son évaluation quantitative, diverses études sur le COS ont été menées sur (1) sa dynamique, en interaction avec les autres constituants du sol selon les pratiques et modes d’usage des terres ou (2) sur sa variabilité spatio-temporelle. Ces études ont été effect..

A global spectral library to characterize the world's soil

Soil provides ecosystem services, supports human health and habitation, stores carbon and regulates emissions of greenhouse gases. Unprecedented pressures on soil from degradation and urbanization are threatening agro-ecological balances and food security. It is important that we learn more about soil to sustainably manage and preserve it for future generations. To this end, we developed and analyzed a global soil visible-near infrared (vis-NIR) spectral library. It is currently the largest and most diverse database of its kind. We show that the information encoded in the spectra can describe soil composition and be associated to land cover and its global geographic distribution, which acts as a surrogate for global climate variability. We also show the usefulness of the global spectra for predicting soil attributes such as soil organic and inorganic carbon, clay, silt, sand and iron contents, cation exchange capacity, and pH. Using wavelets to treat the spectra, which were recorded in different laboratories using different spectrometers and methods, helped to improve the spectroscopic modelling. We found that modelling a diverse set of spectra with a machine learning algorithm can find the local relationships in the data to produce accurate predictions of soil properties. The spectroscopic models that we derived are parsimonious and robust, and using them we derived a harmonized global soil attribute dataset, which might serve to facilitate research on soil at the global scale. This spectroscopic approach should help to deal with the shortage of data on soil to better understand it and to meet the growing demand for information to assess and monitor soil at scales ranging from regional to global. New contributions to the library are encouraged so that this work and our collaboration might progress to develop a dynamic and easily updatable database with better global coverage. We hope that this work will reinvigorate our community's discussion towards larger, more coordinated collaborations. We also hope that use of the database will deepen our understanding of soil so that we might sustainably manage it and extend the research outcomes of the soil, earth and environmental sciences towards applications that we have not yet dreamed of

Carbone des sols en Afrique

Les sols sont une ressource essentielle à préserver pour la production d’aliments, de fibres, de biomasse, pour la filtration de l’eau, la préservation de la biodiversité et le stockage du carbone. En tant que réservoirs de carbone, les sols sont par ailleurs appelés à jouer un rôle primordial dans la lutte contre l’augmentation de la concentration de gaz à effet de serre. Ils sont ainsi au centre des objectifs de développement durable (ODD) des Nations unies, notamment les ODD 2 « Faim zéro », 13 « Lutte contre le changement climatique », 15 « Vie terrestre », 12 « Consommation et production responsables » ou encore 1 « Pas de pauvreté ». Cet ouvrage présente un état des lieux des sols africains dans toute leur diversité, mais au-delà, il documente les capacités de stockage de carbone selon les types de sols et leurs usages en Afrique. Il propose également des recommandations autour de l’acquisition et de l’interprétation des données, ainsi que des options pour préserver, voire augmenter les stocks de carbone dans les sols. Tous les chercheurs et acteurs du développement impliqués dans les recherches sur le rôle du carbone des sols sont concernés par cette synthèse collective. Fruit d’une collaboration entre chercheurs africains et européens, ce livre insiste sur la nécessité de prendre en compte la grande variété des contextes agricoles et forestiers africains pour améliorer nos connaissances sur les capacités de stockage de carbone des sols et lutter contre le changement climatique

Vis-NIR Spectroscopy and PLS Regression with Waveband Selection for Estimating the Total C and N of Paddy Soils in Madagascar

Visible and near-infrared (Vis-NIR) diffuse reflectance spectroscopy with partial least squares (PLS) regression is a quick, cost-effective, and promising technology for predicting soil properties. The advantage of PLS regression is that all available wavebands can be incorporated in the model, while earlier studies indicate that PLS models include redundant wavelengths, and selecting specific wavebands can refine PLS analyses. This study evaluated the performance of PLS regression with waveband selection using Vis-NIR reflectance spectra to estimate the total carbon (TC) and total nitrogen (TN) in soils collected mainly from the surface of upland and lowland rice fields in Madagascar (n = 59; after outliers were removed). We used iterative stepwise elimination-based PLS (ISE-PLS) to estimate soil TC and TN and compared the predictive ability with standard full-spectrum PLS (FS-PLS). The predictive abilities were assessed using the coefficient of determination (R2), the root mean squared error of cross-validation (RMSECV), and the residual predictive deviation (RPD). Overall, ISE-PLS using first derivative reflectance (FDR) showed a better predictive accuracy than ISE-PLS for both TC (R2 = 0.972, RMSECV = 0.194, RPD = 5.995) and TN (R2 = 0.949, RMSECV = 0.019, RPD = 4.416) in the soil of Madagascar. The important wavebands for estimating TC (12.59% of all wavebands) and TN (3.55% of all wavebands) were selected from all 2001 wavebands over the 400–2400 nm range using ISE-PLS. These findings suggest that ISE-PLS based on Vis-NIR diffuse reflectance spectra can be used to estimate soil TC and TN contents in Madagascar with an improved predictive accuracy

Comparing near and mid-infrared reflectance spectroscopy for determining properties of Malagasy soils, using global or LOCAL calibration

Nowadays, near infrared (NIR) and mid-infrared (mid-IR) reflectance spectroscopy are recognised useful approaches for quantifying soil properties, cost and time effectively. The aim of this work was to compare predictions of soil carbon (C) and nitrogen (N) content, C/N ratio, substrate-induced respiration (SIR) and denitrifying enzyme activity (DEA) using NIR and mid-IR spectroscopy over a diverse set of 360 Malagasy topsoils. Partial least square regression was used for fitting NIR and mid-IR spectra to conventional data through procedures of calibration either global (one prediction model for all samples) or LOCAL (one prediction model per sample). Prediction accuracy was assessed according to validation (r(2)), standard error of prediction (SEP) in proportion to the mean and ratio of standard deviation to SEP (RPD). Using both NIR and mid-IR spectroscopy, global calibration over the whole sample set yielded predictions that were excellent for C and N (r(2) > 0.9, SEP= 3), good for C/N, acceptable for SIR, but poor for DEA. LOCAL calibration improved C/N and SIR predictions with both NIR and mid-IR spectroscopy, while DEA prediction became acceptable with NIR spectroscopy only. Additional improvement was achieved when LOCAL calibration was carried out over the fine-textured sub-set, especially for SIR (r(2)>0.9, SEP3). In contrast, LOCAL calibration over the coarse-textured sub-set was clearly not useful for improving prediction accuracy. NIR outperformed mid-IR spectroscopy whatever the variable, the calibration procedure and the sample set (except for SIR over the coarse-textured sub-set, where both similar), suggesting its possible superiority for tropical soils