Changes in plant species richness distribution in Tibetan alpine grasslands under different precipitation scenarios

Species richness is the core of biodiversity-ecosystem functioning (BEF) research. Nevertheless, it is difficult to accurately predict changes in plant species richness under different climate scenarios, especially in alpine biomes. In this study, we surveyed plant species richness from 2009 to 2017 in 75 alpine meadows (AM), 199 alpine steppes (AS), and 71 desert steppes (DS) in the Tibetan Autonomous Region, China. Along with 20 environmental factors relevant to species settlement, development, and survival, we first simulated the spatial pattern of plant species richness under current climate conditions using random forest modelling. Our results showed that simulated species richness matched well with observed values in the field, showing an evident decrease from meadows to steppes and then to deserts. Summer precipitation, which ranked first among the 20 environmental factors, was further confirmed to be the most critical driver of species richness distribution. Next, we simulated and compared species richness patterns under four different precipitation scenarios, increasing and decreasing summer precipitation by 20% and 10%, relative to the current species richness pattern. Our findings showed that species richness in response to altered precipitation was grassland-type specific, with meadows being sensitive to decreasing precipitation, steppes being sensitive to increasing precipitation, and deserts remaining resistant. In addition, species richness at low elevations was more sensitive to decreasing precipitation than to increasing precipitation, implying that droughts might have stronger influences than wetting on species composition. In contrast, species richness at high elevations (also in deserts) changed slightly under different precipitation scenarios, likely due to harsh physical conditions and small species pools for plant recruitment and survival. Finally, we suggest that policymakers and herdsmen pay more attention to alpine grasslands in central Tibet and at low elevations where species richness is sensitive to precipitation changes

Comparison and phylogenetic analysis based on the B2L gene of orf virus from goats and sheep in China during 2009-2011

As a zoonotic infectious disease, orf outbreaks have been reported in China in recent years. However, molecular epidemiology analysis has not been performed for Chinese orf virus (ORFV) strains. Here, we have identified 13 ORFVs from goats and sheep in China between 2009 and 2011. Thirty-four complete B2L sequences were used to construct a phylogenetic tree to elucidate the molecular epidemiology of ORFV in China. Nucleotide sequences of B2L genes of clinical samples and attenuated vaccine strains were aligned and compared. Three genotypes were found by molecular epidemiology analysis. Amino acid substitutions were dispersed among B2 polypeptides from wild and attenuated ORFV strains. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s00705-013-1946-6) contains supplementary material, which is available to authorized users

Braking penalized receding horizon control of heavy haul trains

Incorporated with a receding horizon control (RHC) approach, a penalty method is proposed to reduce energy wasted by braking in a heavy haul train’s operation. The train’s practical nonlinear model is linearized to design the RHC controller. This controller is then applied to the train practical nonlinear dynamics and its performances are analyzed. In particular, the main focus in this study is on the brake penalty’s impact on the train performances. Meantime, a fence method is presented to tackle two issues. The first one is that all the cars in a train cannot be controlled individually due to limit of available transmission channels for control systems in a long train. The other one is that the RHC approach suffers from heavy computation and memory load. Simulations verified that the brake penalty presented in the design can reduce a train’s energy consumption and intrain forces remarkably without sacrificing the train’s velocity tracking performance. Simulations also verified that the fence method is essential to reduce the related computation load when the RHC approach is applied to a long heavy haul train. Further, it is demonstrated that the fence method can effectively shorten computation time and reduce memoryhttp://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979hb2014ai201

Development of an optimal operation approach in the MPC framework for heavy-haul trains

An operation control approach for heavy haul trains to optimize their performance, including operation safety, service quality and energy consumption, is proposed. Following a model predictive control method, the controller is capable of scheduling a train to operate optimally during a long section of the rail track. In the cost function, two penalty factors are presented, one for the braking forces and one for coupler damping effects. The penalty for braking forces is employed to reduce energy waste incurred by braking. The penalty for coupler damping is introduced to alleviate the cyclic vibration of couplers, which link adjacent cars in the train. The damping penalty is also expected to reduce energy wasted by coupler damping and corresponding maintenance/replacement cost of the dampers. In addition, the weight of the velocity tracking term in the objective function is modified to vary dynamically according to the train’s velocity to improve the train’s overall performance. Simulations verify the effectiveness of the proposed control approach. Discussions over the impacts of the two penalty factors and dynamic weight method are provided together with some suggestions on their applications.http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979hb201

HAT: Hybrid Attention Transformer for Image Restoration

Transformer-based methods have shown impressive performance in image restoration tasks, such as image super-resolution and denoising. However, we find that these networks can only utilize a limited spatial range of input information through attribution analysis. This implies that the potential of Transformer is still not fully exploited in existing networks. In order to activate more input pixels for better restoration, we propose a new Hybrid Attention Transformer (HAT). It combines both channel attention and window-based self-attention schemes, thus making use of their complementary advantages. Moreover, to better aggregate the cross-window information, we introduce an overlapping cross-attention module to enhance the interaction between neighboring window features. In the training stage, we additionally adopt a same-task pre-training strategy to further exploit the potential of the model for further improvement. Extensive experiments have demonstrated the effectiveness of the proposed modules. We further scale up the model to show that the performance of the SR task can be greatly improved. Besides, we extend HAT to more image restoration applications, including real-world image super-resolution, Gaussian image denoising and image compression artifacts reduction. Experiments on benchmark and real-world datasets demonstrate that our HAT achieves state-of-the-art performance both quantitatively and qualitatively. Codes and models are publicly available at https://github.com/XPixelGroup/HAT.Comment: Extended version of HA

Diagnosis and phylogenetic analysis of Orf virus from goats in China: a case report

<p>Abstract</p> <p>Background</p> <p>Orf virus (ORFV) is the etiological agent of contagious pustular dermatitis and is the prototype of the genus Parapoxvirus (PPV). It causes a severe exanthematous dermatitis that afflicts domestic and wild small ruminants.</p> <p>Case presentation</p> <p>In the present study, an outbreak of proliferative dermatitis in farmed goats. The presence of ORFV in tissue scrapings from the lips was confirmed by B2L gene polymerase chain reaction (PCR) amplification. The molecular characterization of the ORFV was performed using PCR amplification, DNA sequencing and phylogenetic analysis of the B2L gene.</p> <p>Conclusion</p> <p>The results of this investigation indicated that the outbreak was caused by infection with an ORFV that was closely related genetically to Nantou (DQ934351), which was isolated from the Tai wan province of China and Hoping (EU935106), which originated from South Korea in 2008. This is the first report of the phylogenetic analysis of ORFV from goats in China.</p