PRECONDITIONING AND THE APPLICATION OF CONVOLUTIONAL NEURAL NETWORKS TO CLASSIFY MOVING TARGETS IN SAR IMAGERY

Synthetic Aperture Radar (SAR) is a principle that uses transmitted pulses that store and combine scene echoes to build an image that represents the scene reflectivity. SAR systems can be found on a wide variety of platforms to include satellites, aircraft, and more recently, unmanned platforms like the Global Hawk unmanned aerial vehicle. The next step is to process, analyze and classify the SAR data. The use of a convolutional neural network (CNN) to analyze SAR imagery is a viable method to achieve Automatic Target Recognition (ATR) in military applications. The CNN is an artificial neural network that uses convolutional layers to detect certain features in an image. These features correspond to a target of interest and train the CNN to recognize and classify future images. Moving targets present a major challenge to current SAR ATR methods due to the “smearing” effect in the image. Past research has shown that the combination of autofocus techniques and proper training with moving targets improves the accuracy of the CNN at target recognition. The current research includes improvement of the CNN algorithm and preconditioning techniques, as well as a deeper analysis of moving targets with complex motion such as changes to roll, pitch or yaw. The CNN algorithm was developed and verified using computer simulation.Lieutenant, United States NavyApproved for public release. Distribution is unlimited

A Simplified Equation for Modeling Sediment Transport Capacity

Sediment transport capacity for shallow overland flow was represented as a quadratic function of downslope distance using the assumption of a linear increase in overland flow discharge with downslope distance and an approximation to the Yalin equation for sediment transport capacity. The simplified equation for sediment transport applies to complex topography having uniform soil and management characteristics. The simplified equation accurately approximated the Yalin equation when calibrated using the average of the hydraulic shear stresses at the end of a constant slope reference profile and the end of the actual profile. The simplified equation is useful in deriving closed-form solutions to the governing erosion equations for steady state conditions and reduces the computational time when numerical solutions are required

A Simplified Equation for Modeling Sediment Transport Capacity

Sediment transport capacity for shallow overland flow was represented as a quadratic function of downslope distance using the assumption of a linear increase in overland flow discharge with downslope distance and an approximation to the Yalin equation for sediment transport capacity. The simplified equation for sediment transport applies to complex topography having uniform soil and management characteristics. The simplified equation accurately approximated the Yalin equation when calibrated using the average of the hydraulic shear stresses at the end of a constant slope reference profile and the end of the actual profile. The simplified equation is useful in deriving closed-form solutions to the governing erosion equations for steady state conditions and reduces the computational time when numerical solutions are required

A ranking of hydrological signatures based on their predictability in space

Hydrological signatures are now used for a wide range of purposes, including catchment classification, process exploration and hydrological model calibration. The recent boost in the popularity and number of signatures has however not been accompanied by the development of clear guidance on signature selection. Here we propose that exploring the predictability of signatures in space provides important insights into their drivers, their sensitivity to data uncertainties, and is hence useful for signature selection. We use three complementary approaches to compare and rank 15 commonly‐used signatures, which we evaluate in 671 US catchments from the CAMELS data set (Catchment Attributes and MEteorology for Large‐sample Studies). Firstly, we employ machine learning (random forests) to explore how attributes characterizing the climatic conditions, topography, land cover, soil and geology influence (or not) the signatures. Secondly, we use simulations of a conceptual hydrological model (Sacramento) to benchmark the random forest predictions. Thirdly, we take advantage of the large sample of CAMELS catchments to characterize the spatial auto‐correlation (using Moran's I) of the signature field. These three approaches lead to remarkably similar rankings of the signatures. We show i) that signatures with the noisiest spatial pattern tend to be poorly captured by hydrological simulations, ii) that their relationship to catchments attributes are elusive (in particular they are not correlated to climatic indices) and iii) that they are particularly sensitive to discharge uncertainties. We suggest that a better understanding of their drivers and better characterization of their uncertainties would increase their value in hydrological studies

Atrial septum fat deposition and atrial anatomy assessed by cardiac magnetic resonance: relationship to atrial electrophysiology

To assess the prevalence of fat deposition in the atrial septum with and its relationship with 12-lead electrocardiogram (ECG) atrial parameters (PR interval, P wave duration) and the presence of atrial fibrillation

Infectious Complications Are Associated With Alterations in the Gut Microbiome in Pediatric Patients With Acute Lymphoblastic Leukemia

Acute lymphoblastic leukemia is the most common pediatric cancer. Fortunately, survival rates exceed 90%, however, infectious complications remain a significant issue that can cause reductions in the quality of life and prognosis of patients. Recently, numerous studies have linked shifts in the gut microbiome composition to infection events in various hematological malignances including acute lymphoblastic leukemia (ALL). These studies have been limited to observing broad taxonomic changes using 16S rRNA gene profiling, while missing possible differences within microbial functions encoded by individual species. In this study we present the first combined 16S rRNA gene and metagenomic shotgun sequencing study on the gut microbiome of an independent pediatric ALL cohort during treatment. In this study we found distinctive differences in alpha diversity and beta diversity in samples from patients with infectious complications in the first 6 months of therapy. We were also able to find specific species and functional pathways that were significantly different in relative abundance between samples that came from patients with infectious complications. Finally, machine learning models based on patient metadata and bacterial species were able to classify samples with high accuracy (84.09%), with bacterial species being the most important classifying features. This study strengthens our understanding of the association between infection and pediatric acute lymphoblastic leukemia treatment and warrants further investigation in the future

Localization and function of the renal calcium-sensing receptor

The ability to monitor changes in the ionic composition of the extracellular environment is a crucial feature that has evolved in all living organisms. The cloning and characterization of the extracellular calcium-sensing receptor (CaSR) from the mammalian parathyroid gland in the early 1990s provided the first description of a cellular, ion-sensing mechanism. This finding demonstrated how cells can detect small, physiological variations in free ionized calcium (Ca 2+) in the extracellular fluid and subsequently evoke an appropriate biological response by altering the secretion of parathyroid hormone (PTH) that acts on PTH receptors expressed in target tissues, including the kidney, intestine, and bone. Aberrant Ca 2+ sensing by the parathyroid glands, as a result of altered CaSR expression or function, is associated with impaired divalent cation homeostasis. CaSR activators that mimic the effects of Ca 2+ (calcimimetics) have been designed to treat hyperparathyroidism, and CaSR antagonists (calcilytics) are in development for the treatment of hypercalciuric disorders. The kidney expresses a CaSR that might directly contribute to the regulation of many aspects of renal function in a PTH-independent manner. This Review discusses the roles of the renal CaSR and the potential impact of pharmacological modulation of the CaSR on renal function

Enrichment of Organic Carbon in Sediment Transport by Interrill and Rill Erosion Processes

Erosion and loss of organic carbon (OC) result in degradation of the soil surface. Rill and interrill erosion processes on a silt loam soil were examined in laboratory rainfall and flume experiments. These experiments showed that rill and interrill erosion processes have contrasting impacts on enrichment of OC in transported sediment. Rill erosion was found to be nonselective, while for interrill erosion the enrichment ratio of OC, EROC, varied between 0.9 and 2.6 and was inversely related to the unit sediment discharge. At unit sediment discharge values >0.0017 kg s(-1) m(-1), the EROC remained equal to 1. The enrichment process was not influenced by raindrop impact. Enrichment of OC by "aggregate stripping" was found to be unimportant in our study. This was attributed to the low aggregate stability of the soil and the equal distribution of OC within the different soil aggregate classes

Surface Energy Budgets of Arctic Tundra During Growing Season

This study analyzed summer observations of diurnal and seasonal surface energy budgets across several monitoring sites within the Arctic tundra underlain by permafrost. In these areas, latent and sensible heat fluxes have comparable magnitudes, and ground heat flux enters the subsurface during short summer intervals of the growing period, leading to seasonal thaw. The maximum entropy production (MEP) model was tested as an input and parameter parsimonious model of surface heat fluxes for the simulation of energy budgets of these permafrost‐underlain environments. Using net radiation, surface temperature, and a single parameter characterizing the thermal inertia of the heat exchanging surface, the MEP model estimates latent, sensible, and ground heat fluxes that agree closely with observations at five sites for which detailed flux data are available. The MEP potential evapotranspiration model reproduces estimates of the Penman‐Monteith potential evapotranspiration model that requires at least five input meteorological variables (net radiation, ground heat flux, air temperature, air humidity, and wind speed) and empirical parameters of surface resistance. The potential and challenges of MEP model application in sparsely monitored areas of the Arctic are discussed, highlighting the need for accurate measurements and constraints of ground heat flux.Plain Language SummaryGrowing season latent and sensible heat fluxes are nearly equal over the Arctic permafrost tundra regions. Persistent ground heat flux into the subsurface layer leads to seasonal thaw of the top permafrost layer. The maximum energy production model accurately estimates the latent, sensible, and ground heat flux of the surface energy budget of the Arctic permafrost regions.Key PointThe MEP model is parsimonious and well suited to modeling surface energy budget in data‐sparse permafrost environmentsPeer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/150560/1/jgrd55584.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/150560/2/jgrd55584_am.pd

Soil erosion modelling: A bibliometric analysis

Soil erosion can present a major threat to agriculture due to loss of soil, nutrients, and organic carbon. Therefore, soil erosion modelling is one of the steps used to plan suitable soil protection measures and detect erosion hotspots. A bibliometric analysis of this topic can reveal research patterns and soil erosion modelling characteristics that can help identify steps needed to enhance the research conducted in this field. Therefore, a detailed bibliometric analysis, including investigation of collaboration networks and citation patterns, should be conducted. The updated version of the Global Applications of Soil Erosion Modelling Tracker (GASEMT) database contains information about citation characteristics and publication type. Here, we investigated the impact of the number of authors, the publication type and the selected journal on the number of citations. Generalized boosted regression tree (BRT) modelling was used to evaluate the most relevant variables related to soil erosion modelling. Additionally, bibliometric networks were analysed and visualized. This study revealed that the selection of the soil erosion model has the largest impact on the number of publication citations, followed by the modelling scale and the publication\u27s CiteScore. Some of the other GASEMT database attributes such as model calibration and validation have negligible influence on the number of citations according to the BRT model. Although it is true that studies that conduct calibration, on average, received around 30% more citations, than studies where calibration was not performed. Moreover, the bibliographic coupling and citation networks show a clear continental pattern, although the co-authorship network does not show the same characteristics. Therefore, soil erosion modellers should conduct even more comprehensive review of past studies and focus not just on the research conducted in the same country or continent. Moreover, when evaluating soil erosion models, an additional focus should be given to field measurements, model calibration, performance assessment and uncertainty of modelling results. The results of this study indicate that these GASEMT database attributes had smaller impact on the number of citations, according to the BRT model, than anticipated, which could suggest that these attributes should be given additional attention by the soil erosion modelling community. This study provides a kind of bibliographic benchmark for soil erosion modelling research papers as modellers can estimate the influence of their paper