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Abstract Manatees are aquatic mammals with voracious appetites. They rely on sea grass as the main food source, and often spend up to eight hours a day grazing. They move slow and frequently stay in groups (i.e. aggregations) in shallow water to search for food, making them vulnerable to environment change and other risks. Accurate counting manatee aggregations within a region is not only biologically meaningful in observing their habit, but also crucial for designing safety rules for boaters, divers, etc., as well as scheduling nursing, intervention, and other plans. In this paper, we propose a deep learning based crowd counting approach to automatically count number of manatees within a region, by using low quality images as input. Because manatees have unique shape and they often stay in shallow water in groups, water surface reflection, occlusion, camouflage etc. making it difficult to accurately count manatee numbers. To address the challenges, we propose to use Anisotropic Gaussian Kernel (AGK), with tunable rotation and variances, to ensure that density functions can maximally capture shapes of individual manatees in different aggregations. After that, we apply AGK kernel to different types of deep neural networks primarily designed for crowd counting, including VGG, SANet, Congested Scene Recognition network (CSRNet), MARUNet etc. to learn manatee densities and calculate number of manatees in the scene. By using generic low quality images extracted from surveillance videos, our experiment results and comparison show that AGK kernel based manatee counting achieves minimum Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The proposed method works particularly well for counting manatee aggregations in environments with complex background. 

Abstract. Soil represents the largest phosphorus (P) stock in terrestrialecosystems. Determining the amount of soil P is a critical first step inidentifying sites where ecosystem functioning is potentially limited by soilP availability. However, global patterns and predictors of soil total Pconcentration remain poorly understood. To address this knowledge gap, weconstructed a database of total P concentration of 5275 globallydistributed (semi-)natural soils from 761 published studies. We quantifiedthe relative importance of 13 soil-forming variables in predicting soiltotal P concentration and then made further predictions at the global scaleusing a random forest approach. Soil total P concentration variedsignificantly among parent material types, soil orders, biomes, andcontinents and ranged widely from 1.4 to 9630.0 (median 430.0 and mean570.0) mg kg−1 across the globe. About two-thirds (65 %) of theglobal variation was accounted for by the 13 variables that we selected,among which soil organic carbon concentration, parent material, mean annualtemperature, and soil sand content were the most important ones. Whilepredicted soil total P concentrations increased significantly with latitude,they varied largely among regions with similar latitudes due to regionaldifferences in parent material, topography, and/or climate conditions. SoilP stocks (excluding Antarctica) were estimated to be 26.8 ± 3.1 (mean ± standard deviation) Pg and 62.2 ± 8.9 Pg (1 Pg = 1 × 1015 g) in the topsoil (0–30 cm) and subsoil (30–100 cm), respectively.Our global map of soil total P concentration as well as the underlyingdrivers of soil total P concentration can be used to constraint Earth systemmodels that represent the P cycle and to inform quantification of globalsoil P availability. Raw datasets and global maps generated in this studyare available at https://doi.org/10.6084/m9.figshare.14583375(He et al., 2021). 

To enable an in-depth study of active cyber-physical distribution network, a cyber subsystem model is urgently needed to describe the performance in distribution communication. The methods for quantifying the interactions between subsystems, especially indirect interactions, have not been adequately studied in the existing research. In this paper, a novel model is developed to evaluate the validity of cyber link considering dynamic routing, delay, and communication error, particularly the cyber traffic. Then, an analytical method is presented to quantify the impact of cyber faults considering the functionality validity during distribution automation. And the reliability of cyber and physical subsystems is evaluated based on Non-Sequential and Sequential Monte Carlo methods respectively. Finally, a test system for reliability evaluation is established to analyze the influences of cyber faults. Also sensitivity analyses on the impact of cyber network traffic, element failure rate, and network topology and access communication technology are carried out. The obtained results could provide useful insights into planning and operation of active cyber-physical distribution networks.