Onboard and External Magnetic Bias Estimation for UAS through CDGNSS/Visual Cooperative Navigation

This paper describes a calibration technique aimed at combined estimation of onboard and external magnetic disturbances for small Unmanned Aerial Systems (UAS). In particular, the objective is to estimate the onboard horizontal bias components and the external magnetic declination, thus improving heading estimation accuracy. This result is important to support flight autonomy, even in environments characterized by significant magnetic disturbances. Moreover, in general, more accurate attitude estimates provide benefits for georeferencing and mapping applications. The approach exploits cooperation with one or more “deputy” UAVs and combines drone-to-drone carrier phase differential GNSS and visual measurements to attain magnetic-independent attitude information. Specifically, visual and GNSS information is acquired at different heading angles, and bias estimation is modelled as a non-linear least squares problem solved by means of the Levenberg–Marquardt method. An analytical error budget is derived to predict the achievable accuracy. The method is then demonstrated in flight using two customized quadrotors. A pointing analysis based on ground and airborne control points demonstrates that the calibrated heading estimate allows obtaining an angular error below 1°, thus resulting in a substantial improvement against the use of either the non-calibrated magnetic heading or the multi-sensor-based solution of the DJI onboard navigation filter, which determine angular errors of the order of several degrees.

Mapping Evapotranspiration of Agricultural Areas in Ghana

Climate change is having an adverse effect on the environment especially in sub-Sahara Africa, where capacity for natural resource management such as water is very low. The scope of the effect on land use types have to be estimated to inform proper remedy. A combined estimation of transpiration and evaporation from plants and soil is critical to determine annual water requirement for different land use. Evapotranspiration (ET) is a major component in the world hydrological cycle, and understanding its spatial dimensions is critical in evaluating the effects it has on regional land use. A measure of this component is challenging due to variation in rainfall and environmental changes. The mapping evapotranspiration with high resolution and internalized calibration (METRIC) method is employed to create evapotranspiration map for land use, using remotely sensed data by satellite, processed, and analyzed in ArcGIS. Normalized difference vegetation index (NDVI) was related to the availability of water for vegetation on different land use, and the results indicate a high evapotranspiration for vegetated land use with high NDVI than land use with low NDVI.

A Processed EEG based Brain Anesthetic Resistance Index Predicts Postoperative Delirium in Older Adults: A Dual Center Study

Background: Some older adults show exaggerated responses to drugs that act on the brain, such as increased delirium risk in response to anticholinergic drugs. The brain's response to anesthetic drugs is often measured clinically by processed electroencephalogram (EEG) indices. Thus, we developed a processed EEG based-measure of the brain's neurophysiologic resistance to anesthetic dose-related changes, and hypothesized that it would predict postoperative delirium. Methods: We defined the Duke Anesthesia Resistance Scale (DARS) as the average BIS index divided by the quantity 2.5 minus the average age-adjusted end-tidal MAC (aaMAC) inhaled anesthetic fraction. The relationship between DARS and postoperative delirium was analyzed in derivation (Duke; N=69), validation (Mt Sinai; N=70), and combined estimation cohorts (N=139) of older surgical patients (age >/= 65). In the derivation cohort, we identified a threshold relationship between DARS and for delirium and identified an optimal cut point for prediction. Results: In the derivation cohort, the optimal DARS threshold for predicting delirium was 27.0. The delirium rate was 11/49 (22.5%) vs 11/20 (55.0%) and 7/57 (12.3%) vs 6/13 (46.2%) for those with DARS >/= 27 vs those with DARS < 27 in the derivation and validation cohorts respectively. In the combined estimation cohort, multivariable analysis found a significant association of DARS <27.0 with postoperative delirium (OR=4.7; 95% CI: 1.87, 12.0; p=0.001). In the derivation cohort, the DARS had an AUC of 0.63 with sensitivity of 50%, specificity of 81%, positive predictive value of 0.55, and negative predictive value of 0.78. The DARS remained a significant predictor of delirium after accounting for opioid, midazolam, propofol, non-depolarizing neuromuscular blocker, phenylephrine and ketamine dosage, and for nitrous oxide and epidural usage. Conclusions: These results, if confirmed by larger future studies, suggest than an intraoperative processed EEG-based measure of lower brain anesthetic resistance (i.e. DARS <27) could be used in older surgical patients as an independent predictor of postoperative delirium risk.

The small impact of p-hacking marginally significant results on the meta-analytic estimation of effect size

La etiqueta p-hacking (pH) se refiere a un conjunto de prácticas oportunistas destinadas a hacer que sean significativos algunos valores p que deberían ser no significativos. Algunos han argumentado que debemos prevenir y luchar contra el pH por varias razones, especialmente debido a sus posibles efectos nocivos en la evaluación de los resultados de la investigación primaria y su síntesis meta-analítica. Nos focalizamos aquí en el efecto de un tipo específico de pH, centrado en estudios marginalmente significativos, en la estimación combinada del tamaño del efecto en el meta-análisis. Queremos saber cuánto deberíamos preocuparnos por su efecto de sesgo al evaluar los resultados de un meta-análisis. Hemos calculado el sesgo en una variedad de situaciones que parecen realistas en términos de prevalencia y de la definición operativa del pH. Los resultados muestran que en la mayoría de las situaciones analizadas el sesgo es inferior a una centésima (± 0.01), en términos de d o r. Para alcanzar un nivel de sesgo de cinco centésimas (± 0.05), tendría que haber una presencia masiva de este tipo de pH, lo que parece poco realista. Hay muchas buenas razones para luchar contra el pH, pero nuestra conclusión principal es que entre esas razones no se incluye que tenga un gran impacto en la estimación meta-analítica del tamaño del efecto. The label p-hacking (pH) refers to a set of opportunistic practices aimed at making statistically significant p values that should be non-significant. Some have argued that we should prevent and fight against pH for several reasons, especially because of its potential harmful effects on the assessment of both primary research results and their meta-analytical synthesis. We focus here on the effect of a specific type of pH, focused on marginally significant studies, on the combined estimation of effect size in meta-analysis. We want to know how much we should be concerned with its biasing effect when assessing the results of a meta-analysis. We have calculated the bias in a range of situations that seem realistic in terms of the prevalence and the operational definition of pH. The results show that in most of the situations analyzed the bias is less than one hundredth (± 0.01), in terms of d or r. To reach a level of bias of five-hundredths (± 0.05), there would have to be a massive presence of this type of pH, which seems rather unrealistic. There are many good reasons for fighting against pH, but our main conclusion is that among them is not that it has a big impact on the meta-analytical estimation of effect size.