Autonomic responses during a replicable interrogation.

Calculated heart-rate and pulse-volume responses to visual and auditory stimuli associated with a replicable interrogation as stimulus-triggered across-S averages for 2 experimental groups totaling 75 18-25 yr. olds. Certain stressful points within the interrogator's question syntax could be determined from these average evoked responses, and the magnitude of group reactions to specific question-answer items could be scaled. Differential responses to audiotaped and videotaped stimuli were observed, the audio-only format inducing greater and more varied autonomic changes. The actual stress-value of specific question items and the shape of each average response were contingent on the sequence in which the items were presented. Selected subgroups of deceptive and embarrassing answers were isolated and separately evaluated. Implications of these observations for the analysis of individual responses to specific questions are discussed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Stability and least-squares estimation

It is shown that the algorithms for the stability test of linear discrete systems and the algorithm for least-squares estimation are closely related.     

Soil nutrient maps of Sub-Saharan Africa: assessment of soil nutrient content at 250 m spatial resolution using machine learning

Spatial predictions of soil macro and micro-nutrient content across Sub-Saharan Africa at 250 m spatial resolution and for 0–30 cm depth interval are presented. Predictions were produced for 15 target nutrients: organic carbon (C) and total (organic) nitrogen (N), total phosphorus (P), and extractable—phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), sulfur (S), sodium (Na), iron (Fe), manganese (Mn), zinc (Zn), copper (Cu), aluminum (Al) and boron (B). Model training was performed using soil samples from ca. 59,000 locations (a compilation of soil samples from the AfSIS, EthioSIS, One Acre Fund, VitalSigns and legacy soil data) and an extensive stack of remote sensing covariates in addition to landform, lithologic and land cover maps. An ensemble model was then created for each nutrient from two machine learning algorithms—random forest and gradient boosting, as implemented in R packages ranger and xgboost—and then used to generate predictions in a fully-optimized computing system. Cross-validation revealed that apart from S, P and B, significant models can be produced for most targeted nutrients (R-square between 40–85%). Further comparison with OFRA field trial database shows that soil nutrients are indeed critical for agricultural development, with Mn, Zn, Al, B and Na, appearing as the most important nutrients for predicting crop yield. A limiting factor for mapping nutrients using the existing point data in Africa appears to be (1) the high spatial clustering of sampling locations, and (2) missing more detailed parent material/geological maps. Logical steps towards improving prediction accuracies include: further collection of input (training) point samples, further harmonization of measurement methods, addition of more detailed covariates specific to Africa, and implementation of a full spatio-temporal statistical modeling framework.