Micro-sprinkling irrigation simultaneously improves grain yield and protein concentration of winter wheat in the North China Plain

Increased grain yield(GY) and grain protein concentration(GPC) are the two main targets of efforts to improve wheat(Triticum aestivum L.) production in the North China Plain(NCP). We conducted a three-year field experiment in the 2014–2017 winter wheat growing seasons to compare the effects of conventional irrigation practice(CI) and micro-sprinkling irrigation combined with nitrogen(N) fertilizer(MSI) on GY, GPC, and protein yield(PY). Across the three years, GY, GPC, and PY increased by 10.5%–16.7%, 5.4%–8.0%, and 18.8%–24.6%, respectively, under MSI relative to CI. The higher GY under MSI was due primarily to increased thousand-kernel weight(TKW). The chlorophyll content of leaves was higher under MSI during the mid–late grain filling period, increasing the contribution of post-anthesis dry matter accumulation to GY, with consequent increases in total dry matter accumulation and harvest index compared to CI. During the mid–late grain filling period, the canopy temperature was markedly lower and the relative humidity was higher under MSI than under CI. The duration and rate of filling during the mid–late grain filling period were also higher under MSI than CI, resulting in higher TKW. MSI increased the contribution of post-anthesis N accumulation to grain N but reduced the pre-anthesis remobilization of N in leaves, the primary site of photosynthetic activity, possibly helping maintain photosynthate production in leaves during grain filling. Total N at maturity was higher under MSI than CI,although there was little difference in N harvest index. The higher GPC under MSI than under CI was due to a larger increase in grain N accumulation than in GY. Overall, MSI simultaneously increased both GY and GPC in winter wheat grown in the NCP.

Achieving 20% photovoltaic efficiency by manganese doped methylammonium lead halide perovskites

We report a transition metal such as manganese doped methylammonium lead halide perovskite(MA(Pb:Mn)I_(3)) solar cell with an power conversion efficiency(PCE) over 20%. The rational design and fabrication of MA(Pb:Mn)I3 lead to the enhancements of all the photovoltaic parameters. To incorporate Mn can effectively eliminate the trap-assist and bi-molecular recombination. The photo-absorption ability at shorter wavelengths(i.e., less than 500 nm) and charge carrier lifetime can be elaborated. More importantly, the existence of the Mn^(2+)-I~--Mn^(3+)motif contributes for the double exchange effect, giving rise to the charge/spin transport. By a combination of linearly and circularly polarized photo-excitations, we have explicitly determined the role of intrinsic spin–orbit coupling(SOC) in MA(Pb:Mn)I_(3). More dark states are expected to be available for the photocurrent generation. This study may pave the way for deep understandings of transition metals doped hybrid perovskites for highly efficient solar cell applications.

配位组装构筑的磁性核壳复合材料促进CO_(2)高值催化转化

自负载策略作为一种理想的分子催化剂负载方法引起了研究者的广泛关注.该制备策略会得到无定型的块状固体催化材料,导致大量活性催化中心被包埋在材料基体内部,使得反应底物难以靠近催化中心,从而降低了材料的催化效率.形貌调控被认为是增强固体催化材料活性和相应功能的有效策略之一.其中,球状形貌的磁性四氧化三铁纳米颗粒(Fe_(3)O_(4)NPs)因其比表面积较大、来源广泛和较好的磁响应性能而被广泛应用.在已知文献中,催化剂往往是通过后修饰策略进行负载的,但由于负载位点随机、配位模式不明确以及金属易流失等原因,相应负载催化材料的催化活性大大降低.利用配位组装策略对磁性纳米颗粒进行包覆是当前制备球状核壳型自负载复合催化材料的可行方法之一.考虑到三维材料具有多孔结构和更大的比表面积,有利于气体吸附和传质传热,因此可以作为磁性纳米颗粒理想的包覆材料.本文利用四齿咪唑盐和金属(铱或钯)前体,通过原位配位组装包覆策略对Fe_(3)O_(4)NPs直接进行包覆,制备了一系列磁性核壳结构复合型自负载固体分子催化材料.扫描电子显微镜和透射电子显微镜结果表明,此类复合材料呈现出明显的球状核壳结构,其中氮杂环卡宾金属配位聚合物均匀地包覆在磁性Fe_(3)O_(4)NPs表面.能量色散X射线光谱和球差电镜结果表明,金属均匀分散在整个复合材料基质中.X射线光电子能谱显示,包覆后金属的配位环境与相应均相催化剂没有明显改变.N_(2)和CO_(2)吸附结果表明,本文催化材料相比于相应的无定型自负载催化材料具有更大的比表面积和更高的CO_(2)吸附能力.利用CO_(2)加氢和脱氢反应对复合材料的催化性能进行测试,均显示出优于相应均相催化剂和自负载催化材料的催化活性.其中,在CO_(2)加氢制备甲酸的反应中,得到较好的多相催化体系转化数(1.69×10^(6)TON).此外,在甲酸铵脱氢反应中,催化材料可以通过磁性回收循环使用超过11次.进一步对Fe_(3)O_(4)NPs和无定型自负载催化材料的简单物理混合物进行了活性测试,发现只能得到与无定型自负载催化材料类似的催化效果,表明球状核壳结构对复合材料催化性能具有提升作用.综上,本文不仅通过包覆策略实现了催化材料的形貌调控,而且提升了自负载固体分子催化材料的催化性能,为CO_(2)高值化和氢气的存储和释放提供了可能.

Numerical Analysis of Flow-Induced Vibration and Noise Generation in a Variable Cross-Section Channel

Flow channels with a variable cross-section are important components of piping system and are widely used in variousfields of engineering.Using afinite element method and modal analysis theory,flow-induced noise,mode shapes,and structure-borne noise in such systems are investigated in this study.The results demonstrate that the maximum displacement and equivalent stress are located in the part with variable cross-sectional area.The aver-age excitation force on theflow channel wall increases with theflow velocity.The maximum excitation force occurs in the range of 0–20 Hz,and then it decreases gradually in the range of 20–1000 Hz.Additionally,as theflow velocity rises from 1 to 3 m/s,the overall sound pressure level associated with theflow-induced noise grows from 49.37 to 66.37 dB.Similarly,the overall sound pressure level associated with the structure-borne noise rises from 40.27 to 72.20 dB.When theflow velocity is increased,the increment of the structure-borne noise is higher than that of theflow-induced noise.

Recent Development in Defects Engineered Photocatalysts:An Overview of the Experimental and Theoretical Strategies

Recently,defect architectured photocatalysis is proved to be the most versatile choice for high solar to chemical energy conversion processes.Defect engineering strategies are of great demand to effectively tune the electronic microstructure and surface morphologies of semiconductors to boost charge carrier concentration and extend light harvesting capability.This review provides a comprehensive insight to various kinds of defects along with their synthesis procedures and controlling mechanism to uplift photocatalytic activity.In addition,the contribution made by defects and material optimization techniques toward electronic band structure of the photocatalyst,the optimal concentration of defects,the key adsorption processes,charge distribution,and transfer dynamics have been explained in detail.Further,to clarify the relationship between photocatalytic activity and defect states in real,a comprehensive outlook to the versatile photocatalytic applications has been presented to highlight current challenges and future applications.Defect engineering therefore stands as the next step toward advancement in the design and configuration of modern photocatalysts for high efficiency photocatalysis.

Brain Encoding and Decoding in fMRI with Bidirectional Deep Generative Models

Brain encoding and decoding via functional magnetic resonance imaging(fMRI)are two important aspects of visual perception neuroscience.Although previous researchers have made significant advances in brain encoding and decoding models,existing methods still require improvement using advanced machine learning techniques.For example,traditional methods usually build the encoding and decoding models separately,and are prone to overfitting on a small dataset.In fact,effectively unifying the encoding and decoding procedures may allow for more accurate predictions.In this paper,we first review the existing encoding and decoding methods and discuss the potential advantages of a“bidirectional”modeling strategy.Next,we show that there are correspondences between deep neural networks and human visual streams in terms of the architecture and computational rules.Furthermore,deep generative models(e.g.,variational autoencoders(VAEs)and generative adversarial networks(GANs))have produced promising results in studies on brain encoding and decoding.Finally,we propose that the dual learning method,which was originally designed for machine translation tasks,could help to improve the performance of encoding and decoding models by leveraging large-scale unpaired data.

Predicting Lung Cancers Using Epidemiological Data:A Generative-Discriminative Framework

Predictive models for assessing the risk of developing lung cancers can help identify high-risk individuals with the aim of recommending further screening and early intervention.To facilitate pre-hospital self-assessments,some studies have exploited predictive models trained on non-clinical data(e.g.,smoking status and family history).The performance of these models is limited due to not considering clinical data(e.g.,blood test and medical imaging results).Deep learning has shown the potential in processing complex data that combine both clinical and non-clinical information.However,predicting lung cancers remains difficult due to the severe lack of positive samples among follow-ups.To tackle this problem,this paper presents a generative-discriminative framework for improving the ability of deep learning models to generalize.According to the proposed framework,two nonlinear generative models,one based on the generative adversarial network and another on the variational autoencoder,are used to synthesize auxiliary positive samples for the training set.Then,several discriminative models,including a deep neural network(DNN),are used to assess the lung cancer risk based on a comprehensive list of risk factors.The framework was evaluated on over 55000 subjects questioned between January 2014 and December 2017,with 699 subjects being clinically diagnosed with lung cancer between January 2014 and August 2019.According to the results,the best performing predictive model built using the proposed framework was based on DNN.It achieved an average sensitivity of 76.54%and an area under the curve of 69.24%in distinguishing between the cases of lung cancer and normal cases on test sets.

Interface-induced formation of onion-like alloy nanocrystals by defects engineering

The ability to controlled introduction of defects, particularly twin defects in Pt-based nanocrystals （NCs） provides a possibility to regulate the performance of Pt-based nanocatalyst. However, because of the high internal strain energy existed in twinned structures, the fabrication of defects in Pt-based NCs is sufficiently challenging. Here we demonstrate a ＂low-temperature interface-induced assembly＂ approach that provides precise control over Pt-Cu nanoparticles assembled at the hexadecylamine/water interface, yielding onion-like Pt-Cu NCs exposed a high density of twin defects. Moreover, a bending mechanism is proposed to elucidate the appearance of twin defects and lattice expanding （contraction） based on aberration corrected scanning transmission electron microscopy analysis. This work opens new routes to engineer defects in metal- based alloy NCs, enabling more opportunities in catalysis.

Mechanisms of yttrium on the wettability,surface tension and interactions between Ni-20Co-20Cr-10Al-ξY alloys and MgO ceramics

The mechanisms of Y on the wettability,surface tension,and interactions between the Ni-20 Co-20 Cr-10 Al-ξY alloys and MgO ceramics at 1873 K were investigated by sessile drop experiments.The results of nonlinear fitting showed that the equilibrium contact angles and Y concentrations were approximately in accord with the log-normal distribution law.The equilibrium contact angles changed from 101.5°to 140.5°with Y increasing from 0 wt.%to 1.23 wt.%.Cross-sectional microstructure observations revealed that the thermal dissociation of ceramics occurred and the released[O]atoms can react with Y to produce Y_(2)O_(3) reaction layer along three-phase interphase area.Wetting kinetics analyses indicated that surface tension of the melt droplets had been positively correlated with the Y concentrations,and it increased from 737.8–1045.1 mN/m.Meanwhile,the pinning effect of the rough substrate surface on the three-phase line hindered the spreading of the liquid on ceramics.The change in total free energy of the alloys/ceramics system was considered as the key factor affecting the wettability.Moreover,the surface morphology and thermodynamic stability of ceramics also had some influence on the wettability.

Application of a Nanostructured Composite Material Constructed by Self-Assembly of Titanoniobate Nanosheets and Cobalt Porphyrin to Electrocatalytic Reduction of Oxygen

A novel layered nanocomposite was fabricated by the self-assembly of TiNbOs-nanosheets and 5,10,15,20- tetrakis（N-methylpyridinium-4-yl）porphyrinatocobalt（III） （CoTMPyP）. The product was characterized by a variety of analytical techniques such as XRD, EDX, Zeta potential, AFM, UV-vis, IR and SEM, the guest species were intercalated into the interlayer gallery of KTiNbO5 successfully. The electrochemical property of TiNbO5-CoTMPyP as an electrode modifying material was examined by cyclic voltammetry test in PBS solution （pH = 7）, and the hybrid exhibited excellent electrocatalytical property towards oxygen reduction with the peak potential shifting from -0.703 V （bare GCE） to -0.278 V （modified electrode）. The result also indicated that the oxygen molecule was reduced to H2O2 by a two-electron process.

Numerical simulation and behavior prediction of a space net system throughout the capture process: Spread, contact, and close

In this paper,we develop an exhaustive numerical simulator for the dynamic visualization and behavior prediction of the tether-net system during the whole space debris capture phases,including spread,contact,and close.First of all,to perform its geometrically nonlinear deformation,discrete different geometry theory is applied to model the mechanical response of a flexible net.Based on the discretization of the whole structure into multiple vertexes and lines,the internal force and associated Hession are derived in a closed form to solve a series of nonlinear dynamic equations of motion.The spread and deployment of a packaged net can be realized using this well-established net solver.Next,a multidimensional incremental potential formulation is selected to achieve the intersection-free boundary nonlinear contact and collision between the deformable net and rigid debris.Finally,for the closing mechanism analysis,a log-like barrier functional is derived to achieve the nondeviation condition between the ring–rod linkage system.The C2 continuous log barrier functionals constructed for both the contact model and the linkage system are smooth and differentiable,and,therefore,the nonlinear net capture dynamic system can be efficiently solved through a fully implicit time integrator.Overall,as a demonstration,the whole capture process of a defunct satellite using a hexagon net is simulated through our well-established numerical framework.We believe that our comprehensive numerical methods could provide new insight into the optimal design of active debris removal systems and promote further development of the online control of tether tugging systems.

Polyp-PVT:Polyp Segmentation with Pyramid Vision Transformers

Most polyp segmentation methods use convolutional neural networks(CNNs)as their backbone,leading to two key issues when exchanging information between the encoder and decoder:(1)taking into account the differences in contribution between different-level features,and(2)designing an effective mechanism for fusing these features.Unlike existing CNN-based methods,we adopt a transformer encoder,which learns more powerful and robust representations.In addition,considering the image acquisition influence and elusive properties of polyps,we introduce three standard modules,including a cascaded fusion module(CFM),a camouflage identification module(CIM),and a similarity aggregation module(SAM).Among these,the CFM is used to collect the semantic and location information of polyps from high-level features;the CIM is applied to capture polyp information disguised in low-level features,and the SAM extends the pixel features of the polyp area with high-level semantic position information to the entire polyp area,thereby effectively fusing cross-level features.The proposed model,named Polyp-PVT,effectively suppresses noises in the features and significantly improves their expressive capabilities.Extensive experiments on five widely adopted datasets show that the proposed model is more robust to various challenging situations(e.g.,appearance changes,small objects,and rotation)than existing representative methods.The proposed model is available at https://github.com/DengPingFan/Polyp-PVT.

猪链球菌生物被膜形成的耐药机制

猪链球菌病(Streptococcus suis)是一种严重影响各国养猪业发展和人类健康的人兽共患传染病,可以引起败血症、关节炎、脑膜炎等多种疾病,造成巨大的经济损失。猪链球菌生物被膜的形成是导致其致病性和耐药性增加的主要原因。为了预防和治疗猪链球菌病以及解析其耐药的可能机制,深入了解和掌握猪链球菌生物被膜的形成和耐药机制具有重要意义。本文综述了猪链球菌生物被膜形成和耐药机制的最新科学知识,着重从生化因素、生理因素、分子机制和环境改变等方面总结讨论猪链球菌生物被膜的耐药机制,进一步为该病的防治提供科学的理论依据。

Sub-nm ruthenium cluster catalyst for decomposition as an efficient and robust and synthesis of ammonia： Break the ＂size shackles＂

Downsizing to sub-nm is a general strategy to reduce the cost of catalysts. However, theoretical Wulff-constructed model suggests that sub-nm clusters show little activity for various reactions such as ammonia decomposition and ammonia synthesis because of the lack of active sites. As clusters may deviate from the ideal model construction under reaction conditions, a host-guest strategy to synthesize thermally stable 1.0 run monodispersed Ru dusters by the pyrolysis of MIL-101 hosts is reported here to verify the hypothesis. For ammonia decomposition, the activity of the Ru clusters is 25 times higher than that of commercial Ru/active carbon （AC） at full-conversion temperature, while for ammonia synthesis, the activity of the Ru dusters is 500 times as high as that of promoted Ru NPs counterpart. The catalyst also maintains its activities for 40 h without any increase in the size. This model can be used to develop a host-guest strategy for designing thermally stable sub-nm clusters to atomic-efficiently catalyze reactions.

Vegetation distribution pattern in the dam areas along middle-low reach of Lancang-Mekong River in Yunnan Province, China

Lancang-Mekong River Basin is one of ecoregions with rich biodiversity and high ecological values in the world. The basin has been strongly affected by human activities, particularly by dam construction. This study was conducted to investigate the vegetation distribution patterns in the dam areas along middle-low reach of the Lancang-Mekong River in Yunnan Province of China, where eight cascade dams have been planned or are being constructed. To identify the vegetation composition and structure, we sampled 126 quadrats along the transects arrayed vertically to both side of river channel from the year of 2004 to 2010. We found that the forest, shrub and grass communities were widely spread along the riverside. In low reach watershed of the Lancang-Mekong River, the dominated vegetations were grasses and shrubs which were severely disturbed by human activity. In middle reach of the Lancang-Mekong River, the dry-hot valley vegetation was found in the low valley. At high altitude, the pine forest and semi-evergreen seasonal forest were found. As a result of dam construction and operation, the structure and compositions of riparian vegetation were strongly changed. Some plants declined or disappeared due to the alteration of their habitats. The protection or restoration interventions are urgently needed to mitigate the risk of vegetation damage associated with dam projects along middle and low reach of the Lancang-Mekong River.

Hydrogen-bond-assisted transition-metal-free catalytic transformation of amides to esters

The amide C-N cleavage has drawn a broad interest in synthetic chemistry,biological process and pharmaceutical industry.Transition-metal,luxury ligand or excess base were always vital to the transformation.Here,we developed a transition-metalfree hydrogen-bond-assisted esterification of amides with only catalytic amount of base.The proposed crucial role of hydrogen bonding for assisting esterification was control experiments,density functional theory(DFT)calculations and kinetic studies.Besides broad substrate scopes and excellent functional groups tolerance,this base-catalyzed protocol complements the conventional transition-metal-catalyzed esterification of amides and provides a new pathway to catalytic cleavage of amide C–N bonds for organic synthesis and pharmaceutical industry.

Sharing Weights in Shallow Layers via Rotation Group Equivariant Convolutions

The convolution operation possesses the characteristic of translation group equivariance. To achieve more group equivariances, rotation group equivariant convolutions(RGEC) are proposed to acquire both translation and rotation group equivariances.However, previous work paid more attention to the number of parameters and usually ignored other resource costs. In this paper, we construct our networks without introducing extra resource costs. Specifically, a convolution kernel is rotated to different orientations for feature extractions of multiple channels. Meanwhile, much fewer kernels than previous works are used to ensure that the output channel does not increase. To further enhance the orthogonality of kernels in different orientations, we construct the non-maximum-suppression loss on the rotation dimension to suppress the other directions except the most activated one. Considering that the low-level-features benefit more from the rotational symmetry, we only share weights in the shallow layers(SWSL) via RGEC. Extensive experiments on multiple datasets(i.e., Image Net, CIFAR, and MNIST) demonstrate that SWSL can effectively benefit from the higher-degree weight sharing and improve the performances of various networks, including plain and Res Net architectures. Meanwhile, the convolutional kernels and parameters are much fewer(e.g., 75%, 87.5% fewer) in the shallow layers, and no extra computation costs are introduced.