Coal in China: Resources, Uses, and Advanced Coal Technologies

Reviews trends in China's energy demand, coal consumption, energy policy, efforts to reduce reliance on coal, and research on carbon-capture and other advanced technologies. Examines challenges, including the lack of focus on global climate change

An Adjoint-Free Algorithm for CNOPs via Sampling

In this paper, we propose a sampling algorithm based on state-of-the-art statistical machine learning techniques to obtain conditional nonlinear optimal perturbations (CNOPs), which is different from traditional (deterministic) optimization methods. Specifically, the traditional approach requires numerically computing the gradient (first-order information). However, the sampling approach directly reduces the expensive gradient (first-order information) by the objective value (zeroth-order information), which also avoids using the adjoint technique that requires large amounts of storage and is unusable for many atmosphere and ocean models. We present an intuitive analysis for the sampling algorithm and a rigorous Chernoff-type concentration inequality to probabilistically approximate the exact gradient. The experiments are implemented to obtain the CNOPs for two numerical models, the Burgers equation with small viscosity and the Lorenz-96 model. We demonstrate the CNOPs obtained with their spatial structures, objective values, computation times and nonlinear error growth. Compared with the performance of the three approaches, the CNOPs' spatial structures, objective values, and nonlinear error growth is nearly consistent, while the computation time using the sampling approach with fewer samples is extremely shorter. In other words, the new sampling approach from state-of-the-art statistical machine learning techniques shortens the computation time to the utmost at the cost of losing little accuracy.Comment: 20 pages, 6 figures, 4 table

Monthly Change of Nutrients impact on Phytoplankton in Kuroshio of East China Sea

AbstractUsing the Nutrients data from World Ocean Atlas 2009 issued by NOAA in 2010 to analyze the monthly change of nutrient content, nutrient proportion and nutrient limitation in Kuroshio East China Sea, the results show that: (1)The ratios of N/P, Si/N, Si/P in shallow waters of 250m with a significant spatial differences. The spatial differences of Si/N and N/P are most obvious in March to April and August to September, the smaller differences occurs in October to December. The spatial distribution of Si/P is different from N/P and Si/N, Strong regional differences appears in September, the relatively uniform spatial distribution appears in May to June. (2)The major nutrient concentrations limit in Kuroshio of East China Sea are N and P, which impact in shallow of 300m. The nutrient concentrations are higher than threshold concentration in 300m to deeper area, so it could not appear the phenomenon of nutrient limitation. Phytoplankton growth mainly occurs in the range of 100–200m in April to November

EKF/UKF-based channel estimation for robust and reliable communications in V2V and IIoT

Cyber-physical systems (CPSs) are characterized by integrating computation, communication, and physical system. In typical CPS application scenarios, vehicle-to-vehicle (V2V) and Industry Internet of Things (IIoT), due to doubly selective fading and non-stationary channel characteristics, the robust and reliable end-to-end communication is extremely important. Channel estimation is a major signal processing technology to ensure robust and reliable communication. However, the existing channel estimation methods for V2V and IIoT cannot effectively reduce intercarrier interference (ICI) and lower the computation complexity, thus leading to poor robustness. Aiming at this challenge, according to the channel characteristics of V2V and IIoT, we design two channel estimation methods based on the Bayesian filter to promote the robustness and reliability of end-to-end communication. For the channels with doubly selective fading and non-stationary characteristics of V2V and IIoT scenarios, in the one hand, basis extended model (BEM) is used to further reduce the complexity of the channel estimation algorithm under the premise that ICI can be eliminated in the channel estimation. On the other hand, aiming at the non-stationary channel, a channel estimation and interpolation method based on extended Kalman filter (EKF) and unscented Kalman filter (UKF) Bayesian filters to jointly estimate the channel impulse response (CIR) and time-varying time domain autocorrelation coefficient is adopted. Through the MATLAB simulation, the robustness and reliability of end-to-end communication for V2V and IIoT are promoted by the proposed algorithms

SKFlow: Learning Optical Flow with Super Kernels

Optical flow estimation is a classical yet challenging task in computer vision. One of the essential factors in accurately predicting optical flow is to alleviate occlusions between frames. However, it is still a thorny problem for current top-performing optical flow estimation methods due to insufficient local evidence to model occluded areas. In this paper, we propose the Super Kernel Flow Network (SKFlow), a CNN architecture to ameliorate the impacts of occlusions on optical flow estimation. SKFlow benefits from the super kernels which bring enlarged receptive fields to complement the absent matching information and recover the occluded motions. We present efficient super kernel designs by utilizing conical connections and hybrid depth-wise convolutions. Extensive experiments demonstrate the effectiveness of SKFlow on multiple benchmarks, especially in the occluded areas. Without pre-trained backbones on ImageNet and with a modest increase in computation, SKFlow achieves compelling performance and ranks $\textbf{1st}$ among currently published methods on the Sintel benchmark. On the challenging Sintel clean and final passes (test), SKFlow surpasses the best-published result in the unmatched areas ($7.96$ and $12.50$) by $9.09\%$ and $7.92\%$. The code is available at \href{https://github.com/littlespray/SKFlow}{https://github.com/littlespray/SKFlow}.Comment: Accepted to NeurIPS 202

Magnesium sulphate attenuate remifentanil-induced postoperative hyperalgesia via regulating tyrosine phosphorylation of the NR2B subunit of the NMDA receptor in the spinal cord

Data of PWTL and PWMT values in the study. (XLS 19 kb