Customization of a Commercially Available Prep Scale SFC System to Provide Enhanced Capabilities

Preparative Scale Supercritical Fluid Chromatography is emerging as a powerful alternative to HPLC for the purification and separation of complex chemical reaction mixtures. Advantages include greatly reduced solvent usage (and thus lower cost and environmental impact), higher throughput, and in some cases higher resolution. While there are commercially available prep SFC instruments, none currently offer all the features desired by many medicinal chemists engaged in the drug discovery process. These include: the ability to collect an unlimited number of fractions per sample with high recovery and negligible carryover, fully automated capacity to collect several hundred fractions, and the ability to collect fractions into the same disposable test tubes and racks which are already employed in HPLC. This article describes the customization of a preparatory scale SFC system purchased from Berger Instruments, Inc., Newark, DE. (a subsidiary Mettler-Toledo International, Inc., of Greifensee, Switzerland) in order to provide these capabilities.     

Multilevel Robustness for 2D Vector Field Feature Tracking,Selection and Comparison

Critical point tracking is a core topic in scientific visualization for understanding the dynamic behaviour of time-varying vector field data. The topological notion of robustness has been introduced recently to quantify the structural stability of critical points, that is, the robustness of a critical point is the minimum amount of perturbation to the vector field necessary to cancel it. A theoretical basis has been established previously that relates critical point tracking with the notion of robustness, in particular, critical points could be tracked based on their closeness in stability, measured by robustness, instead of just distance proximity within the domain. However, in practice, the computation of classic robustness may produce artifacts when a critical point is close to the boundary of the domain; thus, we do not have a complete picture of the vector field behaviour within its local neighbourhood. To alleviate these issues, we introduce a multilevel robustness framework for the study of 2D time-varying vector fields. We compute the robustness of critical points across varying neighbourhoods to capture the multiscale nature of the data and to mitigate the boundary effect suffered by the classic robustness computation. We demonstrate via experiments that such a new notion of robustness can be combined seamlessly with existing feature tracking algorithms to improve the visual interpretability of vector fields in terms of feature tracking, selection and comparison for large-scale scientific simulations. We observe, for the first time, that the minimum multilevel robustness is highly correlated with physical quantities used by domain scientists in studying a real-world tropical cyclone dataset. Such an observation helps to increase the physical interpretability of robustness.     

Psychometric properties of the Hawaiian Culture Scale—Adolescent Version.

This article reports on the psychometric properties of a newly constructed Hawaiian Culture Scale—Adolescent Version. A total of 2,272 Native Hawaiian and 1,170 non-Hawaiian adolescents were administered this 50-item inventory that measures the source of learning the Hawaiian way of life, how much Hawaiian beliefs are valued and important to maintain, how much non-Hawaiian beliefs are valued, Hawaiian blood quantum, and specific cultural traditions (seven subscales?=?Lifestyles, Customs, Activities, Folklore, Causes—Locations, Causes—Access, and Language Proficiency). The results strongly supported the internal consistency and validity of the measures for both the Hawaiian and non-Hawaiian groups. On the basis of the demographic data, partial support was offered for cross-cultural theories of ethnic identity. More consistent support was found for multiculturalism in Hawai'i's adolescents. Further research is needed to link these measures with indicators of psychological adjustment (e.g., depression, anxiety, substance use). (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Phase Transitions on Fixed Connected Graphs and Random Graphs in the Presence of Noise

In this paper, we study the phase transition behavior emerging from the interactions among multiple agents in the presence of noise. We propose a simple discrete-time model in which a group of non-mobile agents form either a fixed connected graph or a random graph process, and each agent, taking bipolar value either +1 or -1, updates its value according to its previous value and the noisy measurements of the values of the agents connected to it. We present proofs for the occurrence of the following phase transition behavior: At a noise level higher than some threshold, the system generates symmetric behavior (vapor or melt of magnetization) or disagreement; whereas at a noise level lower than the threshold, the system exhibits spontaneous symmetry breaking (solid or magnetization) or consensus. The threshold is found analytically. The phase transition occurs for any dimension. Finally, we demonstrate the phase transition behavior and all analytic results using simulations. This result may be found useful in the study of the collective behavior of complex systems under communication constraints.     

Thermodynamic modeling of the Na–Al–Ti–H system and Ti dissolution in sodium alanates

Thermodynamic assessments were made to optimize thermodynamic models and parameter fits to selected experimental and first principles hypothetical predicted phase data within the Na–Al–Ti–H system. This enabled thermodynamic modeling of Ti solubility within the sodium alanates: NaAlH₄ and Na₃AlH₆, and the relative stability of Ti-bearing phases. The modeling provides insights into the role of Ti originating from Ti-based activating agents commonly referred to as ‘catalysts’ in promoting reversibility of the Na–Al–H dehydrogenation and rehydrogenation reactions under moderate temperature and pressure conditions relevant to H storage applications. Preliminary assessments were made to evaluate H solubility in bcc-Ti and hcp-Ti, and stability of the hydride δ-TiH₂. To model possible Ti dissolution in NaAlH₄ and α-Na₃AlH₆, sub-lattice models were applied. A repulsive interaction is predicted by first principles calculations when Ti is dissolved in NaAlH₄ or α-Na₃AlH₆, which becomes stronger with increasing temperature. Although Ti is virtually insoluble in NaAlH₄ or α-Na₃AlH₆, a small addition of TiCl₃ will induce a thermodynamic driving force for formation of TiH₂ and/or TiAl₃. The addition of pure Ti shows a weaker effect than TiCl₃ and leads to formation of TiH₂ only. Based on a combined interpretation of present thermodynamic modeling and prior experimental observations, the TiAl₃ and TiH₂ phases are ascribed to have a catalytic effect, not a thermodynamic destabilization effect, on the reversibility of the dehydrogenation/rehydrogenation reactions in the Na–Al–H system.     

Computing depth maps from descent images

In the exploration of the planets of our solar system, images taken during a landers descent to the surface of a planet provide a critical link between orbital images and surface images. The descent images not only allow us to locate the landing site in a global coordinate frame, but they also provide progressively higher-resolution maps for mission planning. This paper addresses the generation of depth maps from the descent images. Our approach has two steps, motion refinement and depth recovery. During motion refinement, we use an initial motion estimate in order to avoid the intrinsic motion ambiguity. The objective of the motion-refinement step is to adjust the motion parameters such that the reprojection error is minimized. The depth-recovery step correlates adjacent frames to match pixels for triangulation. Due to the descending motion, the conventional rectification process is replaced by a set of anti-aliasing image warpings corresponding to a set of virtual parallel planes. We demonstrate experimental results on synthetic and real descent images.Received: 7 November 2002, Accepted: 13 September 2004, Published online: 25 February 2005 Correspondence to: Clark F. Olson     

TRAVOS: Trust and Reputation in the Context of Inaccurate Information Sources

In many dynamic open systems, agents have to interact with one another to achieve their goals. Here, agents may be self-interested, and when trusted to perform an action for another, may betray that trust by not performing the action as required. In addition, due to the size of such systems, agents will often interact with other agents with which they have little or no past experience. There is therefore a need to develop a model of trust and reputation that will ensure good interactions among software agents in large scale open systems. Against this background, we have developed TRAVOS (Trust and Reputation model for Agent-based Virtual OrganisationS) which models an agent’s trust in an interaction partner. Specifically, trust is calculated using probability theory taking account of past interactions between agents, and when there is a lack of personal experience between agents, the model draws upon reputation information gathered from third parties. In this latter case, we pay particular attention to handling the possibility that reputation information may be inaccurate.     

Efficient Pose Clustering Using a Randomized Algorithm

Pose clustering is a method to perform object recognition by determining hypothetical object poses and finding clusters of the poses in the space of legal object positions. An object that appears in an image will yield a large cluster of such poses close to the correct position of the object. If there are m model features and n image features, then there are O(m ³ n ³ ) hypothetical poses that can be determined from minimal information for the case of recognition of three-dimensional objects from feature points in two-dimensional images. Rather than clustering all of these poses, we show that pose clustering can have equivalent performance for this case when examining only O(mn) poses, due to correlation between the poses, if we are given two correct matches between model features and image features. Since we do not usually know two correct matches in advance, this property is used with randomization to decompose the pose clustering problem into O(n ² ) problems, each of which clusters O(mn) poses, for a total complexity of O(mn ³ ) . Further speedup can be achieved through the use of grouping techniques. This method also requires little memory and makes the use of accurate clustering algorithms less costly. We use recursive histograming techniques to perform clustering in time and space that is guaranteed to be linear in the number of poses. Finally, we present results demonstrating the recognition of objects in the presence of noise, clutter, and occlusion.