Contribution of leaf specular reflection to canopy reflectance under black soil case using stochastic radiative transfer model

Numerous canopy radiative transfer models have been proposed based on the assumption of “ideal bi-Lambertian leaves” with the aim of simplifying the interactions between photons and vegetation canopies. This assumption may cause discrepancy between the simulated and measured canopy bidirectional reflectance factor (BRF). Few studies have been devoted to evaluate the impacts of such assumption on simulation of canopy BRF at a high-to-medium spatial resolution (∼30 m). This paper focuses on quantifying the contribution of leaf specular reflection on the estimation of canopy BRF under a black soil case using one of the most efficient radiative transfer models, the stochastic radiative transfer model. Analyses of field and satellite data collected over the boreal Hyytiälä forest in Finland show that leaf specular reflection may lead to errors of up to 33.1% at 550 nm and 32.8% at 650 nm in terms of relative root mean square error. The results suggest that, in order to minimize these errors, leaf specular reflection should be accounted for in modeling BRF.This research was supported by the Fundamental Research Funds for the Central Universities under Grant No. 531107051063 and Guangxi Natural Science Foundation under Grant No. 2016JJD110017. We would like to thank Dr. Rautiainen Miina and Mottus Matti for sharing the field data and the USGS for making the EO-1 Hyperion hyperspectral data publically available. (531107051063 - Fundamental Research Funds for the Central Universities; 2016JJD110017 - Guangxi Natural Science Foundation)Accepted manuscrip

Critical Care Ultrasonography and Its Application for COVID-19

Ultrasound has developed as an invaluable tool in diagnosis and proper management in the intensive care unit (ICU). Application of critical care ultrasonography is quite distinct from the routine comprehensive diagnostic ultrasound exam, because the urgent setting mandates a goal-directed approach. Performing accurate and efficient critical care ultrasound requires ultrasound providers to first understand the pathophysiology of the disease and related imaging findings, and then follow the protocols to perform a focused ultrasound exam. In the ongoing coronavirus disease 2019 (COVID-19) pandemic, ultrasound plays an essential role in diagnosing and monitoring critically ill COVID-19 patients in the ICU. Our review focuses on the basics and clinical application of critical care ultrasound in diagnosing common lung disease, COVID-19 pulmonary lesions, pediatric COVID-19, and cardiovascular dysfunction as well as its role in ECMO and interventional ultrasonography

Determination of optimal reversed field with maximal electrocaloric cooling by a direct entropy analysis

Application of a negative field on a positively poled ferroelectric sample can enhance the electrocaloric cooling and appears as a promising method to optimize the electrocaloric cycle. Experimental measurements show that the maximal cooling does not appear at the zero-polarization point, but around the shoulder of the P-E loop. This phenomenon cannot be explained by the theory based on the constant total entropy assumption under adiabatic condition. In fact, adiabatic condition does not imply constant total entropy when irreversibility is involved. A direct entropy analysis approach based on work loss is proposed in this work, which takes the entropy contribution of the irreversible process into account. The optimal reversed field determined by this approach agrees with the experimental observations. This study signifies the importance of considering the irreversible process in the electrocaloric cycles

Positive and negative electrocaloric effect in BaTiO3_3 in the presence of defect dipoles

The influence of defect dipoles on the electrocaloric effect (ECE) in acceptor doped BaTiO$_3$ is studied by means of lattice-based Monte-Carlo simulations. A Ginzburg-Landau type effective Hamiltonian is used. Oxygen vacancy-acceptor associates are described by fixed defect dipoles with orientation parallel or anti-parallel to the external field. By a combination of canonical and microcanoncial simulations the ECE is directly evaluated. Our results show that in the case of anti-parallel defect dipoles the ECE can be positive or negative depending on the density of defect dipoles. Moreover, a transition from a negative to positive ECE can be observed from a certain density of anti-parallel dipoles on when the external field increases. These transitions are due to the delicate interplay of internal and external fields, and are explained by the domain structure evolution and related field-induced entropy changes. The results are compared to those obtained by MD simulations employing an {\it{ab initio}} based effective Hamiltonian, and a good qualitative agreement is found. In addition, a novel electrocaloric cycle, which makes use of the negative ECE and defect dipoles, is proposed to enhance the cooling effect

Flight Control Development and Test for an Unconventional VTOL UAV

This chapter deals with the control system development and flight test for an unconventional flight vehicle, namely, a tandem ducted-fan experimental flying platform. The first-principle modeling approach combined with the frequency system identification has been adopted to obtain a high-fidelity dynamics model. It is inherently less stable and difficult to control. To accomplish the required practical flight tasks, the flying vehicle needs to work well even in windy conditions. Moreover, for flight control engineers, simple prescribed multi-loop controller structures are preferred. To handle the multiple problems, a structured velocity controller consisting of two feedback loops is developed, where inner loop provides stability augmentation and decoupling, and the outer loop guarantees desired velocity tracking performance. The simultaneous design of the two-loop controllers under multiple performance requirements in the usual H∞ metrics can be cast as a nonsmooth optimization program. To compensate for changes in plant dynamics across the flight envelope, a smooth and compact polynomial scheduling formula is implemented as a function of the forward flight speed. Both simulations and flight test results have been presented in this work to showcase the potential for the proposed robust nonlinear control system to optimize the performance of UAV, specifically unconventional vehicles

Deep Reinforcement Learning-driven Cross-Community Energy Interaction Optimal Scheduling

In order to coordinate energy interactions among various communities and energy conversions among multi-energy subsystems within the multi-community integrated energy system under uncertain conditions, and achieve overall optimization and scheduling of the comprehensive energy system, this paper proposes a comprehensive scheduling model that utilizes a multi-agent deep reinforcement learning algorithm to learn load characteristics of different communities and make decisions based on this knowledge. In this model, the scheduling problem of the integrated energy system is transformed into a Markov decision process and solved using a data-driven deep reinforcement learning algorithm, which avoids the need for modeling complex energy coupling relationships between multi-communities and multi-energy subsystems. The simulation results show that the proposed method effectively captures the load characteristics of different communities and utilizes their complementary features to coordinate reasonable energy interactions among them. This leads to a reduction in wind curtailment rate from 16.3% to 0% and lowers the overall operating cost by 5445.6 Yuan, demonstrating significant economic and environmental benefits.Comment: in Chinese language, Accepted by Electric Power Constructio

Tailoring the electrocaloric effect by internal bias fields and field protocols

In acceptor doped ferroelectrics and in ferroelectric films and nanocomposites, defect dipoles, strain gradients, and the electric boundary conditions at interfaces and surfaces often impose internal bias fields. In this work we delicately study the impact of internal bias fields on the electrocaloric effect (ECE), utilizing an analytical model and \emph{ab initio}-based molecular dynamics simulations. We reveal the complex dependency of the ECE on field protocol and relative strength of internal and external fields. The internal fields may even reverse the sign of the response (inverse or negative ECE). We explore the transition between conventional and inverse ECE and discuss reversible and irreversible contributions to the field-induced specific entropy change. Most importantly, we predict design routes to optimize the cooling and heating response for small external fields by the combination of internal field strengths and the field loading protocol

MASANet: Multi-Angle Self-Attention Network for Semantic Segmentation of Remote Sensing Images

As an important research direction in the field of pattern recognition, semantic segmentation has become an important method for remote sensing image information extraction. However, due to the loss of global context information, the effect of semantic segmentation is still incomplete or misclassified. In this paper, we propose a multi-angle self-attention network (MASANet) to solve this problem. Specifically, we design a multi-angle self-attention module to enhance global context information, which uses three angles to enhance features and takes the obtained three features as the inputs of self-attention to further extract the global dependencies of features. In addition, atrous spatial pyramid pooling (ASPP) and global average pooling (GAP) further improve the overall performance. Finally, we concatenate the feature maps of different scales obtained in the feature extraction stage with the corresponding feature maps output by ASPP to further extract multi-scale features. The experimental results show that MASANet achieves good segmentation performance on high-resolution remote sensing images. In addition, the comparative experimental results show that MASANet is superior to some state-of-the-art models in terms of some widely used evaluation criteria