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.

     

一种自适应人工蜂群算法求解U型顺序相依拆卸线平衡问题

产品拆卸过程中零部件之间会相互干扰影响任务作业时间,基于该情形构建了多目标U型SDDLBP优化模型,并提出一种自适应ABC算法。所提算法设计了自适应动态邻域搜索方法,以提高局部开发能力;采用了轮盘赌与锦标赛法结合的分段选择法,以有效评价并选择蜜源进行深度开发;建立了基于当前最优解的变异操作,以提高全局探索能力快速跳出局部最优。最后,通过算例测试和实例分析验证算法的高效性。     

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.

     

A note on compressive limiting for two‐material flows

In this short note we describe a simple extension to the multi‐material shock‐capturing algorithm presented in (J. Comput. Phys. 2007; 223 :262–297) that can be used to maintain sharp material interfaces. The method takes the form of an artificial compression which is designed so that the material indicator jumps across only a few cells but which does not excite physical instabilities in the flow. The advantages of the approach include its simplicity and flexibility in that it provides a parameter that effectively determines the captured interface thickness. Copyright © 2009 John Wiley & Sons, Ltd.     

Chemical data,ways and means,past and future

A brief outline of various data handling methods, from linear learning machines, principal component analysis, experimental design, and modeling to visualization, optimization, and validation together with a personal view on the historical development of the use of these methods, is given. Some future trends in handling chemical data are proposed as well. Copyright © 2014 John Wiley & Sons, Ltd.     

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.     

不饱和聚酯型人造大理石研究进展

综述了不饱和聚酯型人造大理石的研究进展;回顾了人造大理石的研究现状,指出了其制备研究中存在的性能及工艺缺陷;分析了填料种类、混合填料粒径、黏结剂及制备工艺对人造大理石强度、韧性、斑印及吸水率等性能的影响,并就未来聚酯型人造大理石的性能改进及工艺优化进行了展望.     

Assessment of mobility of heavy metals in two soil types by use of column leaching experiments and chemometric evaluation of elution curves

The objectives of this study were to evaluate the mobility of heavy metals (HMs) in two types of soils (acidic forest soil and neutral agricultural soil) by leaching with calcium chloride solution in column experiments. The screening properties of neutral agricultural soil towards pollution by heavy metals (Ni, Cu, Zn and Cd) are approximately 10 times higher than those of acid forest soil. The neutral agricultural soil, polluted artificially by one pore volume (PV) of an HMs solution of concentration 200 mg L^?1, can screen the leaching of these metals over several hundreds of years. The higher apparent desorption rate and per cent desorption of HMs (especially Cd) in acid forest soil indicated a higher potential of intensive migration of the metals across the profile and indicated potential risk of Cd pollution for this type of soil. The latest approach of artificial neural networks to describe transport of HMs in soil has been also evaluated. Using a simple three-layer perceptron topology with three hidden neurons, the experimental data could be simulated. The results suggested that the pH of soil is a major factor controlling the retention of the heavy metals in the soils.     

文化框架下多群智能优化算法的云作业调度

作业调度是一种云计算核心技术,为了获得更优的云计算作业调度方案,提出一种文化框架下多群智能优化算法的云作业调度方法。首先构建云作业调度问题的数学模型,然后借助文化算法模型,粒子群算法组成信仰空间,人工鱼群算法组成群体空间,两者之间并行演化,相互促进,对云计算作业调度数学模型进行求解,最后通过仿真实验测试算法的性能。结果表明,本文加快了算法的收敛速度,获得了更优的云计算作业调度方案,大幅度缩短少云计算作业完成时间,具有一定的实用价值。