Self-diffusion of low-generation PAMAM dendrimers with hydroxyl surface groups in solutions: a general regularity

An experimental study of the self-diffusion and nuclear magnetic relaxation of poly(amidoamine) dendrimers with hydroxyl surface groups (PAMAM-OH) dissolved in methanol over a wide range of concentration (?) is reported. It is shown that experimental concentration dependences of PAMAM-OH self-diffusion coefficients (D) can be reduced to the so-called generalized ? dependence. Over macromolecular concentration range studied, the generalized concentration dependence of PAMAM-OH D coincides with analogous curve obtained for poly(allylcarbosilane) dendrimers of high generations. This result confirms the existence of common regularities of the dendritic macromolecule self-diffusion, and their independence of the individual physicochemical and structural properties of dendrimer, solvent, and features of their interactions in the given systems. The concentration dependence of the PAMAM-OH diffusion also exhibits a clear signature of an inflexible molecule.     

注气井井筒结垢预测研究

在油田开发过程中,油、气、水和泥浆在地层或管道中流动,当达到一定条件时,油垢、水垢或泥垢便会产生。在实际生产过程中,油垢和泥垢通常伴随着水垢而生成,所以对水垢结垢趋势的预测研究显得至关重要。介绍了Oddo-Tomos饱和指数法的理论预测模型,以及在生产过程中利用Oddo-Tomos饱和指数法预测油田结垢趋势的方法,阐述并分析了油田结垢对油田开采的影响。研究结果可为下一步的生产作业提供理论依据。     

注气井井筒结垢预测研究

在油田开发过程中,油、气、水和泥浆在地层或管道中流动,当达到一定条件时,油垢、水垢或泥垢便会产生。在实际生产过程中,油垢和泥垢通常伴随着水垢而生成,所以对水垢结垢趋势的预测研究显得至关重要。介绍了Oddo-Tomos饱和指数法的理论预测模型,以及在生产过程中利用Oddo-Tomos饱和指数法预测油田结垢趋势的方法,阐述并分析了油田结垢对油田开采的影响。研究结果可为下一步的生产作业提供理论依据。     

Data reconciliation and optimal operation of a catalytic naphtha reformer

The naphtha reforming process converts low-octane gasoline blending components to high-octane components for use in high-performance gasoline fuels. The reformer also has an important function as the producer of hydrogen to the refinery hydrotreaters. A process model based on a unit model structure, is used for estimation of the process condition using data reconciliation. Measurements are classified as redundant or non-redundant and the model variables are classified as observable, barely observable or unobservable. The computed uncertainty of the measured and unmeasured variables shows that even if a variable is observable it may have a very large uncertainty and may thereby be practically unobservable. The process condition at 21 data points, sampled from two years of operation, was reconciled and used to optimize the process operation. There are large seasonal variations in the reformer product price and two operational cases are studied. In case 1, the product price is high and throughput is maximized with respect to process and product quality constraints. In case 2, the product price is low and the throughput is minimized with respect to a low constraint on the hydrogen production. Based on the characteristics of the optimal operation, a “self optimizing” control structure is suggested for each of the two operational cases.     

A new scaling algorithm for the maximum mean cut problem

We present a new scaling algorithm for the maximum mean cut problem. The mean of a cut is defined by the cut capacity divided by the number of arcs crossing the cut. The algorithm uses an approximate binary search and solves the circulation feasibility problem with relaxed capacity bounds. The maximum mean cut problem has recently been studied as a dual analogue of the minimum mean cycle problem in the framework of the minimum cost flow problem by Ervolina and McCormick. A networkN=(G, lower, upper) with lower and upper arc capacities is said to be -feasible ifN has a feasible circulation when we relax the capacity bounds by ; that is, we use (lower(a)- , upper(a)+) bounds instead of (lower(a), upper(a)) bounds for each arca A. During an approximate binary search we maintain two bounds,LB andUB, such thatN is LB-infeasible andUB-feasible, and we reduce the interval size (LB, UB) by at least one-third at each iteration. For a graph withn vertices, m arcs, and integer capacities bounded byU, the running time of this algorithm is O(mn log(nU). This time bound is better than the time achieved by McCormick and Ervolina under thesimilarity condition (that is,U=O(n^o(1))). Our algorithm can be naturally used for the circulation feasibility problem, and thus provides a new scaling algorithm for the minimum cut problem.Research supported by a grant-in-aid of the Ministry of Education, Science and Culture of Japan.     

A sequential model for disaggregating near-surface soil moisture observations using multi-resolution thermal sensors

A sequential model is developed to disaggregate microwave-derived soil moisture from 40 km to 4 km resolution using MODIS (Moderate Imaging Spectroradiometer) data and subsequently from 4 km to 500 m resolution using ASTER (Advanced Scanning Thermal Emission and Reflection Radiometer) data. The 1 km resolution airborne data collected during the three-week National Airborne Field Experiment 2006 (NAFE'06) are used to simulate the 40 km pixels, and a thermal-based disaggregation algorithm is applied using 1 km resolution MODIS and 100 m resolution ASTER data. The downscaled soil moisture data are subsequently evaluated using a combination of airborne and in situ soil moisture measurements. A key step in the procedure is to identify an optimal downscaling resolution in terms of disaggregation accuracy and sub-pixel soil moisture variability. Very consistent optimal downscaling resolutions are obtained for MODIS aboard Terra, MODIS aboard Aqua and ASTER, which are 4 to 5 times the thermal sensor resolution. The root mean square error between the 500 m resolution sequentially disaggregated and ground-measured soil moisture is 0.062 vol./vol. with a bias of − 0.045 vol./vol. and values ranging from 0.08 to 0.40 vol./vol.     

RankVisu: Mapping from the neighborhood network

Most multidimensional scaling methods focus on the preservation of dissimilarities to map high dimensional items in a low-dimensional space. However, the mapping function usually does not consider the preservation of small dissimilarities as important, since the cost is small with respect to the preservation of large dissimilarities. As a consequence, an item's neighborhoods may be sacrificed for the benefit of the overall mapping. We have subsequently designed a mapping method devoted to the preservation of neighborhood ranks rather than their dissimilarities: RankVisu. A mapping of data is obtained in which neighborhood ranks are as close as possible according to the original space.A comparison with both metric and non-metric MDS highlights the pros (in particular, cluster enhancement) and cons of RankVisu.     

Self-similarity of parallel machines

Self-similarity is a property of physical systems that describes how to scale parameters such that dissimilar systems appear to be similar. Computer systems are self-similar if certain ratios of computational forces, also known as computational intensities, are equal. Two machines with different computational power, different network bandwidth and different inter-processor latency behave the same way if they have the same ratios of forces. For the parallel conjugate gradient algorithm studied in this paper, two machines are self-similar if and only if the ratio of one force describing latency effects to another force describing bandwidth effects is the same for both machines. For the two machines studied in this paper, this ratio, which we call the mixing coefficient, is invariant as problem size and processor count change. The two machines have the same mixing coefficient and belong to the same equivalence class.     

Using Similarity Analyses to Scale Out Research Findings Across Andean Watershed Basins

Abstract

Strategic research in agriculture and natural resources carried out by international research centers is deemed a public good and should, sooner or later, be put into the hands of development, governmental and non-governmental organizations. However, this research is usually done at specific pilot sites; there is a greater need to know how representative those sites are in relation to the diversity of contexts in other locations. Such is the case with the Challenge Program on Water and Food (CPWF), a global initiative in water research promoted by the Consultative Group on International Agricultural Research (CGIAR), which is developing and implementing strategic research in nine basins located in the tropics of Africa, Asia and South America. Given that resources are not available to collect data from the whole of the region, pilot sites are needed. It is hoped that research outputs obtained in the selected pilot sites can be the basis for scaling out solutions to similar situations in neighbouring or adjacent areas in same or different basins.

In order to contribute to the scaling-out process, different classification methodologies were applied to determine how specific watershed basins are representative of larger areas. The Andean eco-region served as a case study but the methods can easily be applied in other regions. The spatial diversity of biophysical and social conditions across the Andes requires careful site selection. Two methods, a combination of Weight of Evidence (WofE) and Logistic Regression (LR) methods and Fast Cluster analysis, were used to determine the similarity of selected sites with those excluded. A 1-km study resolution covering most of the Andes eco-region included annual rainfall, elevation, length of growing period, land cover, roads and population density as the key variables. Results showed complementarities between the two methods in presenting a probability surface of similarity across the Andes and a clustering of similar sites inside and outside the pilot basins. The output information forms a strong basis for devising plans to scale out research findings from the pilot basins to the whole region.     

Dimensionality reduction for computer facial animation

This paper describes the usage of dimensionality reduction techniques for computer facial animation. Techniques such as Principal Components Analysis (PCA), Expectation-Maximization (EM) algorithm for PCA, Multidimensional Scaling (MDS), and Locally Linear Embedding (LLE) are compared for the purpose of facial animation of different emotions. The experimental results on our facial animation data demonstrate the usefulness of dimensionality reduction techniques for both space and time reduction. In particular, the EMPCA algorithm performed especially well in our dataset, with negligible error of only 1-2%.