Heat capacity and enthalpy of fusion of pyrimethanil laurate (C24H37N3O2)

Low-temperature heat capacities of pyrimethanil laurate (C24H37N3O2) were precisely measured with an automated adiabatic calorimeter over the temperature range between T = 78 K and T = 340 K. The sample was observed to melt at (321.52 +/- 0.04) K. The molar enthalpy and entropy of fusion as well as the chemical purity of the compound were determined to be (67244 +/- 11) J (.) mol(-1), (209.28 +/- 0.02) J (.) mol(-1) (.) K-1, (0.9943 +/- 0.0004) mass fraction, respectively. The extrapolated melting temperature for the absolutely pure compound obtained from fractional melting experiments was (322.264 +/- 0.006) K. (C) 2004 Elsevier Ltd. All rights reserved.

A data-model fusion approach for upscaling gross ecosystem productivity to the landscape scale based on remote sensing and flux footprint modelling

Chinese Academy of Sciences ; National Science Foundation of China [41071059]; National Key Technology R&D Program of China [2008BAK50B06-02]; National Basic Research Program of China [2010CB950900, 2010CB950704]; Natural Sciences and Engineering Research Council of Canada

Studies on the fusion heat of poly(ether ether ketone ketone)-poly(ether biphenyl ether ketone ketone) copolymers

On the basis of DSC measurements, the Delta H-f(0) values of the fusion heat for PEEKK-PEBEKK copolymers with various biphenyl contents were obtained by using thermodynamics statistical theory proposed by Flory and graphical method of the specific volume-fusion heat. The results reveal that Delta H-f(0) values determined by these two methods for PEEKK-PEBEKK copolymers with various biphenyl content are nearly the same, and that Delta H-f(0) values are closely dependent on biphenyl content. Delta H-f(0) value is minimum at n(B)=0.35.

Lineage-specific domain fusion in the evolution of purine nucleotide cyclases in cyanobacteria

Cyclic nucleotides (both cAMP and cGMP) play extremely important roles in cyanobacteria, such as regulating heterocyst formation, respiration, or gliding. Catalyzing the formation of cAMP and cGMP from ATP and GTP is a group of functionally important enzymes named adenylate cyclases and guanylate cyclases, respectively. To understand their evolutionary patterns, in this study, we presented a systematic analysis of all the cyclases in cyanobacterial genomes. We found that different cyanobacteria had various numbers of cyclases in view of their remarkable diversities in genome size and physiology. Most of these cyclases exhibited distinct domain architectures, which implies the versatile functions of cyanobacterial cyclases. Mapping the whole set of cyclase domain architectures from diverse prokaryotic organisms to their phylogenetic tree and detailed phylogenetic analysis of cyclase catalytic domains revealed that lineage-specific domain recruitment appeared to be the most prevailing pattern contributing to the great variability of cyanobacterial cyclase domain architectures. However, other scenarios, such as gene duplication, also occurred during the evolution of cyanobacterial cyclases. Sequence divergence seemed to contribute to the origin of putative guanylate cyclases which were found only in cyanobacteria. In conclusion, the comprehensive survey of cyclases in cyanobacteria provides novel insight into their potential evolutionary mechanisms and further functional implications.

An application of the analytic hierarchy process and fuzzy logic inference in a decision support system for forage selection

Forage selection plays a prominent role in the process of returning cultivated lands back into grasslands. The conventional method of selecting forage species can only provide attempts for problem-solving without considering the relationships among the decision factors globally. Therefore, this study is dedicated to developing a decision support system to help farmers correctly select suitable forage species for the target sites. After collecting data through a field study, we developed this decision support system. It consists of three steps: (1) the analytic hierarchy process (AHP), (2) weights determination, and (3) decision making. In the first step, six factors influencing forage growth were selected by reviewing the related references and by interviewing experts. Then a fuzzy matrix was devised to determine the weight of each factor in the second step. Finally, a gradual alternative decision support system was created to help farmers choose suitable forage species for their lands in the third step. The results showed that the AHP and fuzzy logic are useful for forage selection decision making, and the proposed system can provide accurate results in a certain area (Gansu Province) of China.

An Obstacle Cluster Based Data Fusion Method for Mobile Robot

首先给出了一种通过融合多个超声波传感器和一台激光全局定位系统的数据建立机器人环境地图的方法 ,并在此基础上 ,首次提出了机器人在非结构环境下识别障碍物的一种新方法 ,即基于障碍物群的方法 .该方法的最大特点在于它可以更加简洁、有效地提取和描述机器人的环境特征 ,这对于较好地实现机器人的导航、避障 ,提高系统的自主性和实时性是至关重要的 .大量的实验结果表明了该方法的有效性 .

Multi-sensors data acquisition and information fusion for manned submersible vehicle

本文介绍了一种用于载人潜水器的导航传感器的数据采集及信息融合技术。航行控制计算机通过基于工业以太网的数据通信系统对各传感器进行数据采集,采用卡尔曼滤波器完成对各传感器数据信息的融合,以便提高数据的精度和控制系统的性能,并将结果送给监控计算机,用于载人潜水器的姿态显示。

A Robust Amorphous Hierarchy from Persistent Nodes

For a very large network deployed in space with only nearby nodes able to talk to each other, we want to do tasks like robust routing and data storage. One way to organize the network is via a hierarchy, but hierarchies often have a few critical nodes whose death can disrupt organization over long distances. I address this with a system of distributed aggregates called Persistent Nodes, such that spatially local failures disrupt the hierarchy in an area proportional to the diameter of the failure. I describe and analyze this system, which has been implemented in simulation.

Learning Classes Correlated to a Hierarchy

Trees are a common way of organizing large amounts of information by placing items with similar characteristics near one another in the tree. We introduce a classification problem where a given tree structure gives us information on the best way to label nearby elements. We suggest there are many practical problems that fall under this domain. We propose a way to map the classification problem onto a standard Bayesian inference problem. We also give a fast, specialized inference algorithm that incrementally updates relevant probabilities. We apply this algorithm to web-classification problems and show that our algorithm empirically works well.

Purification and characterization of recombinant truncated human interleukin-11 expressed as fusion protein in Escherichia coli

Mature human interleukin-11 (HuIL-11) is a cytokine consisting of 178 amino acid residues that results from scission of the N-terminal signal peptide, consisting of 21 amino acid residaues, from the corresponding nascent polypeptide. A DNA fragment encoding a truncated HuIL-11 (trHuIL-11), with an additional 5 amino acid residues removed from the N-terminus, was cloned into vector pGEX-2T between the BamHI site and the EcoRI site. Upon transformation with Escherichia coli BL21, the construct over-produced a glutathione S-transferase (GST)-fused protein in a soluble form after IPTG induction. The fusion protein was initially fractionated with butyl-Sepharose 4 fast flow column and by affinity chromatography using a GSH-Sepharose 4B column. On-site enzymatic release with thrombin gave the target protein at 96% purity as judged by SDS-PAGE and HPLC. Expression of the interleukin as a GST-fused protein thus greatly improved downstream processing. Subsequent biological activity assay suggested that trHuIL-11 had similar activity profile to the naturally produced sample and may be a promising candidate for further development as biopharmaceutical.

Heat capacity and enthalpy of fusion of fenoxycarb

Fenoxycarb was synthesized and its heat capacities were precisely measured with an automated adiabatic calorimeter over the temperature range from 79 to 360 K. The sample was observed to melt at (326.31 +/- 0.14) K. The molar enthalpy and entropy of fusion as well as the chemical purity of the compound were determined to be (26.98 +/- 0.04) kJ-mol(-1), (82.69 +/- 0.09) J-K-1-mol(-1) and 99.53% +/- 0.01%, respectively. The thermodynamic functions relative to the reference temperature (298.15 K) were calculated based on the heat capacity measurements in the temperature range between 80 and 360 K. The extrapolated melting temperature for the absolutely pure compound obtained from fractional melting experiments was (326.62 +/- 0.06) K. Further research on the melting process of this compound was carried out by means of differential scanning calorimetry technique. The result was in agreement with that obtained from the measurements of heat capacities.

Determining Acceptance Possibility for a Quantum Computation is Hard for the Polynomial Hierarchy

It is shown that determining whether a quantum computation has a non-zero probability of accepting is at least as hard as the polynomial time hierarchy. This hardness result also applies to determining in general whether a given quantum basis state appears with nonzero amplitude in a superposition, or whether a given quantum bit has positive expectation value at the end of a quantum computation. This result is achieved by showing that the complexity class NQP of Adleman, Demarrais, and Huang, a quantum analog of NP, is equal to the counting class coC=P.

Information Fusion and Hierarchical Knowledge Discovery by ARTMAP Neural Networks

Mapping novel terrain from sparse, complex data often requires the resolution of conflicting information from sensors working at different times, locations, and scales, and from experts with different goals and situations. Information fusion methods help resolve inconsistencies in order to distinguish correct from incorrect answers, as when evidence variously suggests that an object's class is car, truck, or airplane. The methods developed here consider a complementary problem, supposing that information from sensors and experts is reliable though inconsistent, as when evidence suggests that an objects class is car, vehicle, or man-made. Underlying relationships among objects are assumed to be unknown to the automated system of the human user. The ARTMAP information fusion system uses distributed code representations that exploit the neural network's capacity for one-to-many learning in order to produce self-organizing expert systems that discover hierarchial knowledge structures. The system infers multi-level relationships among groups of output classes, without any supervised labeling of these relationships. The procedure is illustrated with two image examples.

Self-Organizing Hierarchical Knowledge Discovery by an ARTMAP Image Fusion System

Classifying novel terrain or objects front sparse, complex data may require the resolution of conflicting information from sensors working at different times, locations, and scales, and from sources with different goals and situations. Information fusion methods can help resolve inconsistencies, as when evidence variously suggests that an object's class is car, truck, or airplane. The methods described here consider a complementary problem, supposing that information from sensors and experts is reliable though inconsistent, as when evidence suggests that an object's class is car, vehicle, and man-made. Underlying relationships among objects are assumed to be unknown to the automated system or the human user. The ARTMAP information fusion system used distributed code representations that exploit the neural network's capacity for one-to-many learning in order to produce self-organizing expert systems that discover hierarchical knowledge structures. The system infers multi-level relationships among groups of output classes, without any supervised labeling of these relationships.