Climatic and Morphometric Explanatory Variables of Glacier Changes in the Andes (8–55°S): New Insights From Machine Learning Approaches

Over the last decades, glaciers across the Andes have been strongly affected by a loss of mass and surface areas. This increases risks of water scarcity for the Andean population and ecosystems. However, the factors controlling glacier changes in terms of surface area and mass loss remain poorly documented at watershed scale across the Andes. Using machine learning methods (Least Absolute Shrinkage and Selection Operator, known as LASSO), we explored climatic and morphometric variables that explain the spatial variance of glacier surface area variations in 35 watersheds (1980–2019), and of glacier mass balances in 110 watersheds (2000–2018), with data from 2,500 to 21,000 glaciers, respectively, distributed between 8 and 55°S in the Andes. Based on these results and by applying the Partitioning Around Medoids (PAM) algorithm we identified new glacier clusters. Overall, spatial variability of climatic variables presents a higher explanatory power than morphometric variables with regards to spatial variance of glacier changes. Specifically, the spatial variability of precipitation dominates spatial variance of glacier changes from the Outer Tropics to the Dry Andes (8–37°S) explaining between 49 and 93% of variances, whereas across the Wet Andes (40–55°S) the spatial variability of temperature is the most important climatic variable and explains between 29 and 73% of glacier changes spatial variance. However, morphometric variables such as glacier surface area show a high explanatory power for spatial variance of glacier mass loss in some watersheds (e.g., Achacachi with r2 = 0.6 in the Outer Tropics, Río del Carmen with r2 = 0.7 in the Dry Andes). Then, we identified a new spatial framework for hydro-glaciological analysis composed of 12 glaciological zones, derived from a clustering analysis, which includes 274 watersheds containing 32,000 glaciers. These new zones better take into account different seasonal climate and morphometric characteristics of glacier diversity. Our study shows that the exploration of variables that control glacier changes, as well as the new glaciological zones calculated based on these variables, would be very useful for analyzing hydro-glaciological modelling results across the Andes (8–55°S).

A Spatial Variant Motion Compensation Algorithm for High-Monofrequency Motion Error in Mini-UAV-Based BiSAR Systems

High-frequency motion errors can drastically decrease the image quality in mini-unmanned-aerial-vehicle (UAV)-based bistatic synthetic aperture radar (BiSAR), where the spatial variance is much more complex than that in monoSAR. High-monofrequency motion error is a special BiSAR case in which the different motion errors from transmitters and receivers lead to the formation of monofrequency motion error. Furthermore, neither of the classic processors, BiSAR and monoSAR, can compensate for the coupled high-monofrequency motion errors. In this paper, a spatial variant motion compensation algorithm for high-monofrequency motion errors is proposed. First, the bistatic rotation error model that causes high-monofrequency motion error is re-established to account for the bistatic spatial variance of image formation. Second, the corresponding parameters of error model nonlinear gradient are obtained by the joint estimation of subimages. Third, the bistatic spatial variance can be adaptively compensated for based on the error of the nonlinear gradient through contour projection. It is suggested based on the simulation and experimental results that the proposed algorithm can effectively compensate for high-monofrequency motion error in mini-UAV-based BiSAR system conditions.

Marine Debris on New Zealand Beaches - Baseline Data to Evaluate Regional Variances

Terrestrial sources of marine debris on beaches are substantial, increasing, and are primarily a result of mismanaged waste on land. The scale, source, and composition of beached marine debris in New Zealand was determined by surveying 41 beaches, with triplicate belt transects, across the North and South Islands. Results demonstrated a significant spatial variance, with the South Island showing a significantly higher mean density than the more populated North Island by count as well as by weight. The majority of all anthropogenic marine debris detected was plastic and arrived through the water. Explanations for regional variances in debris presence are difficult to ascertain with certainty but could not be explained by population density and proximity. These findings contribute to the understudied field of marine debris research in New Zealand and the Southern Hemisphere and provide a starting point for evidence-based mitigation. Recommended changes to future monitoring programs are made. This first national baseline study of marine debris in New Zealand serves as a reference for follow-up studies, including research at other locations.

THEORIZATION IN IR AND THIRD WORLD STATES: THE PROMISE OF HISTORICAL SOCIOLOGY IN EXPLAINING THE NEXUS BETWEEN DOMESTIC AND INTERNATIONAL SPHERES

Until alternative theories that sought a serious consideration of third world states in International Relations (IR) proliferated in the last quarter of the 20th century, knowledge production remained monopolized by dominant Western-centered theories. Historical Sociology in International Relations (HSIR) is one of the approaches that aimed at directing IR towards more inclusive inquiries that acknowledge temporal and spatial variance; especially against ahistorical and ‘asociological’ foundations of Neorealism. Despite this motivation, most of the studies and debates within HSIR are concentrated on illustrating the approach’s applicability in the study of Western states. Through a review of the available literature, this paper aims to demonstrate the promise of HSIR in explaining the relationship between domestic and foreign affairs of third world states. To achieve this objective, the paper mainly draws from the works of John Hobson and Fred Halliday and suggests the incorporation of third world states in the inquiries and debate within HSIR.

Joint Modeling of Genetics and Field Variation in Plant Breeding Trials Using Relationship and Different Spatial Methods: A Simulation Study of Accuracy and Bias

Modelling field spatial patterns is standard practice for the analysis of plant breeding. Jointly fitting the genetic relationship among individuals and spatial information enables better separability between the variance due to genetics and field variation. This study aims to quantify the accuracy and bias of estimative parameters using different approaches. We contrasted three settings for the genetic term: no relationship (I), pedigree relationship (A), and genomic relationship (G); and a set of approaches for the spatial variation: no-spatial (NS), moving average covariate (MA), row-column adjustment (RC), autoregressive AR1 × AR1 (AR), spatial stochastic partial differential equations, or SPDE (SD), nearest neighbor graph (NG), and Gaussian kernel (GK). Simulations were set to represent soybean field trials at F2:4 generation. Heritability was sampled from a uniform distribution U(0,1). The simulated residual-to-spatial ratio between residual variance and spatial variance (Ve:Vs) ranged from 9:1 to 1:9. Experimental settings were conducted under an augmented block design with the systematic distribution of checks accounting for 10% of the plots. Relationship information had a substantial impact on the accuracy of the genetic values (G > A > I) and contributed to the accuracy of spatial effects (30.63–42.27% improvement). Spatial models were ranked based on an improvement to the accuracy of estimative of genetic effects as SD ≥ GK ≥ AR ≥ NG ≥ MA > RC ≥ NS, and to the accuracy of estimative of spatial effects as GK ≥ SD ≥ NG > AR ≥ MA > RC. Estimates of genetic and spatial variance were generally biased downwards, whereas residual variances were biased upwards. The advent of relationship information reduced the bias of all variance components. Spatial methods SD, AR, and GK provided the least biased estimates of spatial and residual variance.

Global inequality remotely sensed

Economic inequality is notoriously difficult to quantify as reliable data on household incomes are missing for most of the world. Here, we show that a proxy for inequality based on remotely sensed nighttime light data may help fill this gap. Individual households cannot be remotely sensed. However, as households tend to segregate into richer and poorer neighborhoods, the correlation between light emission and economic thriving shown in earlier studies suggests that spatial variance of remotely sensed light per person might carry a signal of economic inequality. To test this hypothesis, we quantified Gini coefficients of the spatial variation in average nighttime light emitted per person. We found a significant relationship between the resulting light-based inequality indicator and existing estimates of net income inequality. This correlation between light-based Gini coefficients and traditional estimates exists not only across countries, but also on a smaller spatial scale comparing the 50 states within the United States. The remotely sensed character makes it possible to produce high-resolution global maps of estimated inequality. The inequality proxy is entirely independent from traditional estimates as it is based on observed light emission rather than self-reported household incomes. Both are imperfect estimates of true inequality. However, their independent nature implies that the light-based proxy could be used to constrain uncertainty in traditional estimates. More importantly, the light-based Gini maps may provide an estimate of inequality where previously no data were available at all.

Advances in Physiological Cardiac Stimulation

Advances in cardiac stimulation demonstrate that bradyarrhythmia treatments go beyond heart rate control. The concern with the ventricular stimulation site and, consequently, with the maintenance of intraventricular synchrony has become routine in most services. Techniques of physiological cardiac stimulation, such as stimulation of the bundle of His and the left branch, have been improved. Despite the indisputable benefits of these therapeutic modalities, there are technical difficulties that limit systematic use. In this sense, to make physiological cardiac stimulation more practical and reproducible, the concept of parahissian stimulation was expanded and studied. The technique, simpler and reproducible, contemplates a conventional approach of the right ventricle. The big difference is the use of QRS spatial variance analysis technology (Synchromax®, Exo S.A., Argentina) to confirm the maintenance of ventricular synchrony according to the implanted site.