基于彩色扫描仪的图像光谱重构

针对彩色扫描仪的特点,采用主元分析法(PCA)和反向传播(BP)人工神经网络(ANN)相结合的方法对图像光谱重构进行研究。选择IT8.7/2标准色卡作为训练样本,将该色卡中的另一组色靶作为检验样本以讨论不同网络结构以及不同主元数和训练样本数对光谱重构的影响,再以自然色系统(NCS)色卡为检验样本来分析不同种类的训练和检验样本与光谱重构性能的关系。实验结果表明,采用3-14-6网络结构和6个主元数是最佳选择,训练样本和扫描目标之间的一致性是基于彩色扫描仪图像光谱重构的关键所在。     

Quantum Anti-Zeno Effect in Artificial Quantum Systems

In this paper, we study a quantum anti-Zeno effect （QAZE） purely induced by repetitive measurements for an artificial atom interacting with a structured bath. This bath can be artificially realized with coupled resonators in one dimension and possesses photonic band structure like Bloeh electron in a periodic potential. In the presence of repetitive measurements, the pure QAZE is discovered as the observable decay is not negligible even for the atomic energy level spacing outside of the energy band of the artificial bath. If there were no measurements, the decay would not happen outside of the band. In this sense, the enhanced decay is completely induced by measurements through the relaxation channels provided by the bath. Besides, we also discuss the controversial golden rule decay rates originated from the van Hove＇s singularities and the effects of the counter-rotating terms.     

太空诱变桔梗的X射线荧光光谱的测定分析

选用X射线荧光光谱法(XRF),对我国独创的第4代太空诱变育种桔梗、地面组桔梗和桔梗对照品的元素种类和含量进行测定与分析。结果表明,太空组桔梗中与桔梗理气、化痰功效相关的Zn,Mn,Fe等元素分别比地面组提高了1.9,2.4,0.6倍,其中Zn和Mn元素比对照品分别提高了0.6和1.9倍。即与地面组相比,太空组桔梗更趋近于或优于对照品桔梗。太空组桔梗中多种元素指标得到了大幅度的提升和优化。XRF方法具有方便快捷、灵敏度高、元素和含量范围广以及再现性好等特点,可用于一切能制成粉末样品的其他中药的测定分析。     

Artificial neural network‐based modeling of snow properties using field data and hyperspectral imagery

This study attempts to model snow wetness and snow density of Himalayan snow cover using a combination of Hyperspectral image processing and Artificial Neural Network (ANN). Initially, a total of 300 spectral signature measurements, synchronized with snow wetness and snow density, were collected in the field. The spectral reflectance of snow was then modeled as a function of snow properties using ANN. Four snow wetness and three snow density models were developed. A strong correlation was observed in near‐infrared and shortwave‐infrared region. The correlation analysis of ANN modeled snow density and snow wetness showed a strong linear relationship with field‐based data values ranging from 0.87–0.90 and 0.88–0.91, respectively. Our results indicate that an Artificial Intelligence (AI) approach, using a combination of Hyperspectral image processing and ANN, can be efficiently used to predict snow properties (wetness and density) in the Himalayan region. Recommendations for resource managers

• Snow properties, such as snow wetness and snow density are mainly investigated through field‐based survey but rugged terrains, difficult weather conditions, and logistics management issues establish remote sensing as an efficient alternative to monitor snow properties, especially in the mountain environment.
• Although Hyperspectral remote sensing is a powerful tool to conduct the quantitative analysis of the physical properties of snow, only a few studies have used hyperspectral data for the estimation of snow density and wetness in the Himalayan region. This could be because of the lack of synchronized snow properties data with field‐based spectral acquisitions.
• In combination with Hyperspectral image processing, Artificial Neural Network (ANN) can be a useful tool for effective snow modeling because of its ability to capture and represent complex input‐output relationships.
• Further research into understanding the applicability of neural networks to determine snow properties is required to obtain results from large snow cover areas of the Himalayan region.

     

Neural network modeling of hydrological systems: A review of implementation techniques

This paper presents a review of procedural steps and implementation techniques used in the development of artificial intelligence models, generally referred to as artificial neural networks (ANNs), within the water resources domain. It focusses on identifying different areas wherein ANNs have found application thereby elucidating its advantages and disadvantages as well as various challenges encountered in its use. Results from this review provide useful insights into how the performance of ANNs can be improved and potential areas of application that are yet to be explored in hydrological modeling. Recommendations for Resource Managers

• Development of integrated and hybrid artificial intelligent tools is critical to achieving improved forecasts in hydrological modeling studies.

• Further research into comprehending the internal mechanisms of neural networks is required to obtain a practical meaning of each network component deployed to solve real‐world problems.

• More robust optimization techniques and tools like differential evolution, particle swarm optimization and deep neural nets, are yet to be fully explored in the water resources analysis, and should be given more attention to enhance neural networks aptitude for modeling complex and nonlinear hydrological processes.

     

Molecular Hydrogels with Esterase-like Activity

The development of molecular hydrogels that can be applied for mimicking bioactive molecules attracts extensive interests of researchers in fields of self‐assembly. In this study, we reported on several molecular hydrogels based on naphthylacetic acid‐peptides containing L‐histidine formed by the heating‐cooling process. All hydrogels exhibited higher activity to hydrolyze 4‐nitrophenyl acetate (4‐NPA) than the free L‐histidine probably due the high density of L‐histidine residue at the surface of self‐assembled nanofibers. To calculate the 4‐NPA hydrolysis rates, the Michaelis‐Menten enzyme kinetics model was made. Among these gels, the gel of Nap‐GFFYGHY possesses the highest enzyme activity of making the ester bond cleavage, which is approximately 25 times higher than that of the control (free L‐histidine and Nap‐GFFYGYY). These results indicate that molecular hydrogels with self‐assembled nanofibers have great potential for the generation of self‐assembled multivalent materials.     

A Doping Technique that Suppresses Undesirable H2 Evolution Derived from Overall Water Splitting in the Highly Selective Photocatalytic Conversion of CO2 in and by Water

Photocatalytic conversion of CO₂ to reduction products, such as CO, HCOOH, HCHO, CH₃OH, and CH₄, is one of the most attractive propositions for producing green energy by artificial photosynthesis. Herein, we found that Ga₂O₃ photocatalysts exhibit high conversion of CO₂. Doping of Zn species into Ga₂O₃ suppresses the H₂ evolution derived from overall water splitting and, consequently, Zn‐doped, Ag‐modified Ga₂O₃ exhibits higher selectivity toward CO evolution than bare, Ag‐modified Ga₂O₃. We observed stoichiometric amounts of evolved O₂ together with CO. Mass spectrometry clarified that the carbon source of the evolved CO is not the residual carbon species on the photocatalyst surface, but the CO₂ introduced in the gas phase. Doping of the photocatalyst with Zn is expected to ease the adsorption of CO₂ on the catalyst surface.     

新型希夫碱型钳形人工受体的微波合成及其对中性分子的识别性能

以对苯二甲酸为起始原料,设计并合成了8个新型的芳环钳形人工受体(3a～3h),其结构经1H NMR,IR,ESI-MS和元素分析表征。利用UV-Vis滴定法初步考察了3a～3h对中性分子(邻苯二胺,间苯二胺,对苯二胺和二苯甲酮)的识别性能,结果表明3a～3h对其具有良好的识别性能。