Esterification and Transesterification of Renewable Chemicals

An overview is given of newly developed Lewis or Brønsted acid and base catalysts for esterification, transesterification and ester interchange reactions. The most relevant applications of these catalysts and reactions in the domain of renewable resources, in particular, oils and fats, are discussed.     

Fischer–Tropsch synthesis: Deactivation of promoted and unpromoted cobalt–alumina catalysts

The loss in activity of Pt-promoted and unpromoted 25 wt% Co–Al₂O₃ catalysts has been compared under identical conditions except for adjustment of the space velocity to give the same initial CO-conversion. Both catalysts underwent a 200 h period of rapid, initial decline in CO conversion and then a slower, linear decline during the next 1000 h. Pt-promotion did not alter the cobalt dispersion (or average particle size) from that of the unpromoted catalyst but did increase the amount of cobalt that was reduced. When compared not by time-on-stream, but by the moles of Co converted per unit weight of catalyst, both the Pt-promoted and unpromoted catalysts decline in activity at the same rate.     

15 years of processing and dissemination of SPOT-VEGETATION products

Throughout the VEGETATION programme, the Flemish Institute for Technological Research (VITO) uninterruptedly hosted the prime user segment of both VEGETATION 1 and VEGETATION 2 multispectral instruments on board the Satellite Pour l’Observation de la Terre 4 (SPOT 4) and SPOT 5 satellites. Operational since the launch of SPOT 4 in March 1998, and foreseen to continue at least until the end of the SPOT 5 mission (anticipated in spring 2014), this user segment comprises a processing facility (PF), actively receiving, processing, correcting, archiving, and distributing the VEGETATION data and derived added-value products. First and foremost, the VEGETATION programme has been serving the needs of operational users – both institutional and commercial – requesting data in near-real time. However, scientific and educational users too benefited significantly, in particular from VEGETATION’s unique time series of the Earth’s land cover, and more specifically the vegetation cover. Over the years, the centralized archive houses processed data covering the equivalent of 11,000 times the Earth’s surface, and delivered more than 50 terapixels to around 10.000 users. As such, VEGETATION’s mission is a prime example of what Europe wants to achieve through the Global Monitoring for Environment and Security (GMES) initiative: truly operational services providing reliable and up-to-date information.

This article describes the processing facility, the way the data and products are archived, the different dissemination channels as well as the data policy adopted and the users served. One of the recent evolutions, the development of an entirely new product distribution facility (PDF), implemented as part of the Project for On-Board Autonomy – Vegetation (PROBA-V) user segment is discussed.     

Spectral Ray Differentials

Light refracted by a dispersive interface leads to beautifully colored patterns that can be rendered faithfully with spectral Monte‐Carlo methods. Regrettably, results often suffer from chromatic noise or banding, requiring high sampling rates and large amounts of memory compared to renderers operating in some trichromatic color space. Addressing this issue, we introduce spectral ray differentials, which describe the change of light direction with respect to changes in the spectrum. In analogy with the classic ray and photon differentials, this information can be used for filtering in the spectral domain. Effectiveness of our approach is demonstrated by filtering for offline spectral light and path tracing as well as for an interactive GPU photon mapper based on splatting. Our results show considerably less chromatic noise and spatial aliasing while retaining good visual similarity to reference solutions with negligible overhead in the order of milliseconds.     

Spatio‐temporal geometry fusion for multiple hybrid cameras using moving least squares surfaces

Multi‐view reconstruction aims at computing the geometry of a scene observed by a set of cameras. Accurate 3D reconstruction of dynamic scenes is a key component for a large variety of applications, ranging from special effects to telepresence and medical imaging. In this paper we propose a method based on Moving Least Squares surfaces which robustly and efficiently reconstructs dynamic scenes captured by a calibrated set of hybrid color+depth cameras. Our reconstruction provides spatio‐temporal consistency and seamlessly fuses color and geometric information. We illustrate our approach on a variety of real sequences and demonstrate that it favorably compares to state‐of‐the‐art methods.     

Approximate Symmetry Detection in Partial 3D Meshes

Symmetry is a common characteristic in natural and man‐made objects. Its ubiquitous nature can be exploited to facilitate the analysis and processing of computational representations of real objects. In particular, in computer graphics, the detection of symmetries in 3D geometry has enabled a number of applications in modeling and reconstruction. However, the problem of symmetry detection in incomplete geometry remains a challenging task. In this paper, we propose a vote‐based approach to detect symmetry in 3D shapes, with special interest in models with large missing parts. Our algorithm generates a set of candidate symmetries by matching local maxima of a surface function based on the heat diffusion in local domains, which guarantee robustness to missing data. In order to deal with local perturbations, we propose a multi‐scale surface function that is useful to select a set of distinctive points over which the approximate symmetries are defined. In addition, we introduce a vote‐based scheme that is aware of the partiality, and therefore reduces the number of false positive votes for the candidate symmetries. We show the effectiveness of our method in a varied set of 3D shapes and different levels of partiality. Furthermore, we show the applicability of our algorithm in the repair and completion of challenging reassembled objects in the context of cultural heritage.     

Hierarchical opacity optimization for sets of 3D line fields

The selection of meaningful lines for 3D line data visualization has been intensively researched in recent years. Most approaches focus on single line fields where one line passes through each domain point. This paper presents a selection approach for sets of line fields which is based on a global optimization of the opacity of candidate lines. For this, existing approaches for single line fields are modified such that significantly larger amounts of line representatives are handled. Furthermore, time coherence is addressed for animations, making this the first approach that solves the line selection problem for 3D time‐dependent flow. We apply our technique to visualize dense sets of pathlines, sets of magnetic field lines, and animated sets of pathlines, streaklines and masslines.     

Necrodols: Anti-insectan terpenes from defensive secretion of carrion beetle (Necrodes surinamensis)

Two compounds recently isolated from the defensive gland ofNecrodes surinamensis, -and -necrodol, first representatives of a new category of monoterpenes (the necrodanes), are shown to be repellent to ants and other insects and irritating to cockroaches and flies. The compounds doubtless play a defensive role inNecrodes. The possible long-range applied significance of research on insect repellents is discussed.Paper No. 80 of the seriesDefense Mechanisms of Arthropods. Paper No. 79 is T. Eisner et al., Experientia 42:204–207 (1986).     

A method for predicting the probability of business network profitability

In the design phase of business collaboration, it is desirable to be able to predict the profitability of the business-to-be. Therefore, techniques to assess qualities such as costs, revenues, risks, and profitability have been previously proposed. However, they do not allow the modeler to properly manage uncertainty with respect to the design of the considered business collaboration. In many real collaboration projects today, uncertainty regarding the business’ present or future characteristics is so significant that ignoring it becomes problematic. In this paper, we propose an approach based on the predictive, probabilistic architecture modeling framework (P²AMF), capable of advanced and probabilistically sound reasoning about profitability risks. The P²AMF-based approach for profitability risk prediction is also based on the e³-value modeling language and on the object constraint language. The paper introduces the prediction and modeling approach, and a supporting software tool. The use of the approach is illustrated by means of a case study originated from the Stockholm Royal Seaport smart city project.