Historical development, impact mechanism and future trends of nitrogen footprint in Wuxi City, China.

Human activities have changed the biogeochemical cycle of nitrogen, leading to a large amount of reactive nitrogen (Nr) into the environment, aggravating a series of environmental problems, affecting human and ecosystem health. Cities are the core areas driving nitrogen cycling in terrestrial ecosystems, however, there are numerous influencing factors and their contributions are unclear. The nitrogen footprint is an important index to understand the impact of human activities on the environment, however, the calculation of urban nitrogen footprint needs a simplified and accurate system method.

Spatio-temporal patterns and drivers of CH and CO fluxes from rivers and lakes in highly urbanized areas.

Gaseous carbon exchange at the water-air interface of rivers and lakes is an essential process for regional and global carbon cycle assessments. Many studies have shown that rivers surrounding urban landscapes can be hotspots for greenhouse gas (GHG) emissions. Here we investigated the variability of diffusive GHG (methane [CH] and carbon dioxide [CO]) emissions from rivers in different landscapes (i.

Design of Fully Spectral CNNs for Efficient FPGA-Based Acceleration.

Computing convolutional layers in the frequency domain using fast Fourier transformation (FFT) has been demonstrated to be effective in reducing the computational complexity of convolutional neural networks (CNNs). Nevertheless, the main challenge of this approach lies in the frequent and repeated transformations between the spatial and frequency domains due to the absence of nonlinear functions in the spectral domain, as such it makes the benefit less attractive for low-latency inference, especially on embedded platforms. To overcome the drawbacks in the existing FFT-based convolution, we propose a fully spectral CNN using a novel spectral-domain adaptive rectified linear unit (ReLU) layer, which completely removes the compute-intensive transformations between the spatial and frequency domains within the network.

Decoding river pollution trends and their landscape determinants in an ecologically fragile karst basin using a machine learning model.

Karst watersheds accommodate high landscape complexity and are influenced by both human-induced and natural activity, which affects the formation and process of runoff, sediment connectivity and contaminant transport and alters natural hydrological and nutrient cycling. However, physical monitoring stations are costly and labor-intensive, which has confined the assessment of water quality impairments on spatial scale. The geographical characteristics of catchments are potential influencing factors of water quality, often overlooked in previous studies of highly heterogeneous karst landscape.

High-Performance Acceleration of 2-D and 3-D CNNs on FPGAs Using Static Block Floating Point.

Over the past few years, 2-D convolutional neural networks (CNNs) have demonstrated their great success in a wide range of 2-D computer vision applications, such as image classification and object detection. At the same time, 3-D CNNs, as a variant of 2-D CNNs, have shown their excellent ability to analyze 3-D data, such as video and geometric data. However, the heavy algorithmic complexity of 2-D and 3-D CNNs imposes a substantial overhead over the speed of these networks, which limits their deployment in real-life applications.

analysis of glycosyltransferase 2 family genes in duckweed () and its role in salt stress tolerance.

Plant glycosyltransferase 2 (GT2) family genes are involved in plant abiotic stress tolerance. However, the roles of GT2 genes in the abiotic resistance in freshwater plants are largely unknown. We identified seven GT2 genes in duckweed, remarkably more than those in the genomes of , , , , , , and , suggesting a significant expansion of this family in the duckweed genome.

Temporal responses of hydrochemical variables and dissolved Fe(II) to flooding at a lake riparian wetland under different vegetation revealing by high resolution DGT.

The interplay between hydrological and biogeochemical processes in riparian wetland was recognized to lead directly to the temporal variations of surface water quality. However, the effects of flooding and vegetation on the release and entrapment of heavy metals and nutrients in riparian wetland remain poorly understood. The study aimed at investigating the influences of flooding and vegetation on the hydrochemical and Fe-redox change in the soil porewater and shallow groundwater, in Poyang lake riparian wetland through hydrochemical monitoring and diffusive gradient technology (DGT).

Toward Full-Stack Acceleration of Deep Convolutional Neural Networks on FPGAs.

Due to the huge success and rapid development of convolutional neural networks (CNNs), there is a growing demand for hardware accelerators that accommodate a variety of CNNs to improve their inference latency and energy efficiency, in order to enable their deployment in real-time applications. Among popular platforms, field-programmable gate arrays (FPGAs) have been widely adopted for CNN acceleration because of their capability to provide superior energy efficiency and low-latency processing, while supporting high reconfigurability, making them favorable for accelerating rapidly evolving CNN algorithms. This article introduces a highly customized streaming hardware architecture that focuses on improving the compute efficiency for streaming applications by providing full-stack acceleration of CNNs on FPGAs.