The role of adsorbate overlayers in thiophene hydrodesulfurization over molybdenum and rhenium single crystals

Thiophene hydrodesulfurization (HDS) has been investigated over model single crystal catalysts of molybdenum and rhenium. Thiophene HDS is a structure sensitive reaction over rhenium and a structure insensitive reaction over molybdenum. Adsorbed sulfur decreases the activity of both Re(0001) and Mo(100) surfaces while adsorbed carbon has distinctly different effects on the catalytic properties of the two metals. Carbon overlayers deactivate the Re(0001) surface but have no effect on the HDS activity of the Mo(100) surface. Radiotracer³⁵S and¹⁴C studies indicate that HDS occurs on an adsorbate overlayer on molybdenum comprised primarily of carbon. HDS over rhenium, on the other hand, occurs on the bare metal surface. This appears to be responsible for the observed differences in the influence of surface structure on HDS activity.     

Characterization of Ceramics by X-Ray and Neutron Small-Angle Scattering

This paper reviews some recent advances in small-angle X-ray and neutron scattering methods and their application to address complex issues in ceramic systems of technological importance. It is shown how small-angle scattering (SAS) can be applied to ceramic systems in order to extract statistically representative microstructure information (e.g., void volume fraction size distributions, internal surface areas, pore morphologies) that complements the information obtained from diffraction methods, X-ray microtomography, or electron microscopy. It is demonstrated how SAS studies provide insights, not obtainable by other means, on the processing–microstructure–property relationships that frequently govern technological performance.     

Automatic and Efficient Human Pose Estimation for Sign Language Videos

We present a fully automatic arm and hand tracker that detects joint positions over continuous sign language video sequences of more than an hour in length. To achieve this, we make contributions in four areas: (i) we show that the overlaid signer can be separated from the background TV broadcast using co-segmentation over all frames with a layered model; (ii) we show that joint positions (shoulders, elbows, wrists) can be predicted per-frame using a random forest regressor given only this segmentation and a colour model; (iii) we show that the random forest can be trained from an existing semi-automatic, but computationally expensive, tracker; and, (iv) introduce an evaluator to assess whether the predicted joint positions are correct for each frame. The method is applied to 20 signing footage videos with changing background, challenging imaging conditions, and for different signers. Our framework outperforms the state-of-the-art long term tracker by Buehler et al. (International Journal of Computer Vision 95:180–197, 2011), does not require the manual annotation of that work, and, after automatic initialisation, performs tracking in real-time. We also achieve superior joint localisation results to those obtained using the pose estimation method of Yang and Ramanan  (Proceedings of the IEEE conference on computer vision and pattern recognition, 2011).     

Deblurring Shaken and Partially Saturated Images

We address the problem of deblurring images degraded by camera shake blur and saturated (over-exposed) pixels. Saturated pixels violate the common assumption that the image-formation process is linear, and often cause ringing in deblurred outputs. We provide an analysis of ringing in general, and show that in order to prevent ringing, it is insufficient to simply discard saturated pixels. We show that even when saturated pixels are removed, ringing is caused by attempting to estimate the values of latent pixels that are brighter than the sensor’s maximum output. Estimating these latent pixels is likely to cause large errors, and these errors propagate across the rest of the image in the form of ringing. We propose a new deblurring algorithm that locates these error-prone bright pixels in the latent sharp image, and by decoupling them from the remainder of the latent image, greatly reduces ringing. In addition, we propose an approximate forward model for saturated images, which allows us to estimate these error-prone pixels separately without causing artefacts. Results are shown for non-blind deblurring of real photographs containing saturated regions, demonstrating improved deblurred image quality compared to previous work.     

A computationally efficient norm optimal iterative learning control approach for LTV systems

This paper proposes a computationally efficient iterative learning control (ILC) approach termed non-lifted norm optimal ILC (N-NOILC). The objective is to remove the computational complexity issues of previous 2-norm optimal ILC approaches, which are based on lifted system techniques, while retaining the iteration domain convergence properties. The computational complexity needed to implement the proposed method scales linearly with the trial length. Therefore, the approach can be implemented on controlled processes having long trial durations and high sampling rates. Robustness is accomplished by adding a penalty term on the control input in the cost function. Simulations are presented to verify and validate the features of the proposed method.     

The ups and downs of protein topology; rapid comparison of protein structure

Protein topology can be described at different levels. At themost fundamental level, it is a sequence of secondary structureelements (a `primary topology string'). Searching predictedprimary topology strings against a library of strings from knownprotein structures is the basis of some protein fold recognitionmethods. Here a method known as TOPSCAN is presented for rapidcomparison of protein structures. Rather than a simple two-letteralphabet (encoding strand and helix), more complex alphabetsare used encoding direction, proximity, accessibility and lengthof secondary elements and loops in addition to secondary structure.Comparisons are made between the structural information contentof primary topology strings and encodings which contain additionalinformation (`secondary topology strings'). The algorithm isextremely fast, with a scan of a large domain against a libraryof more than 2000 secondary structure strings completing in     

Increased α-linolenic acid intake increases tissue α-linolenic acid content and apparent oxidation with little effect on tissue docosahexaenoic acid in the guinea pig

The essential fatty acids do not have identical roles in nutrition. Linoleic acid (LA) accumulates throughout the body of most mammals, whereas α-linolenic acid (ALA) is rarely found in tissue lipids to the same extent as LA. It has been argued that this is the result of metabolism of ALA to docosahexaenoic acid (DHA) or that ALA is rapidly β-oxidized to acetyl CoA and CO₂. In this study, we consider the effect of high and low ALA levels on the tissue distribution of ALA and other n-3 polyunsaturated fatty acids (PUFA) in all tissues. Guinea pigs were fed one of two defined diets for 3 wk from wearning with both diets containing 1.8% (by weight) of LA and either 1.7% ALA or 0.03% ALA. The high ALA diet was associated with significantly increased ALA levels in all tissues except the brain and significantly increased levels of long-chain n-3 PUFA in all tissues except intestines, brain, carcass, and skin. The long-chain n-3 PUFA content of the whole body was less than 5% of that of the ALA content in both diet groups, and the major long-chain n-3 PUFA (>66% of total) in the body was 22∶5n−3. The brain was the only tissue where the DHA content exceeded that of 22∶5n−3. On the low ALA diet, there appeared to be conservation of ALA based on a comparison of the ratio of LA to ALA in the tissues compared with that in the diet. On the high ALA diet there was a loss of ALA relative to LA in the tissues compared with the diet. These studies suggest that the low levels of tissue ALA in the guinea pig are likely the result of β-oxidation or excretion via the skin and fur rather than metabolism to DHA.     

Perceiving,learning, and exploiting object affordances for autonomous pile manipulation

Autonomous manipulation in unstructured environments will enable a large variety of exciting and important applications. Despite its promise, autonomous manipulation remains largely unsolved. Even the most rudimentary manipulation task—such as removing objects from a pile—remains challenging for robots. We identify three major challenges that must be addressed to enable autonomous manipulation: object segmentation, action selection, and motion generation. These challenges become more pronounced when unknown man-made or natural objects are cluttered together in a pile. We present a system capable of manipulating unknown objects in such an environment. Our robot is tasked with clearing a table by removing objects from a pile and placing them into a bin. To that end, we address the three aforementioned challenges. Our robot perceives the environment with an RGB-D sensor, segmenting the pile into object hypotheses using non-parametric surface models. Our system then computes the affordances of each object, and selects the best affordance and its associated action to execute. Finally, our robot instantiates the proper compliant motion primitive to safely execute the desired action. For efficient and reliable action selection, we developed a framework for supervised learning of manipulation expertise. To verify the performance of our system, we conducted dozens of trials and report on several hours of experiments involving more than 1,500 interactions. The results show that our learning-based approach for pile manipulation outperforms a common sense heuristic as well as a random strategy, and is on par with human action selection.     

Towards a Trajectory Planning Concept: Augmenting Path Planning Methods by Considering Speed Limit Constraints

Trajectory planning is an essential part of systems controlling autonomous entities such as vehicles or robots. It requires not only finding spatial curves but also that dynamic properties of the vehicles (such as speed limits for certain maneuvers) must be followed. In this paper, we present an approach for augmenting existing path planning methods to support basic dynamic constraints, concretely speed limit constraints. We apply this approach to the well known A* and state-of-the-art Theta* and Lazy Theta* path planning algorithms. We use a concept of trajectory planning based on a modular architecture in which spatial and dynamic parts can be easily implemented. This concept allows dynamic aspects to be processed during planning. Existing systems based on a similar concept usually add dynamics (velocity) into spatial curves in a post-processing step which might be inappropriate when the curves do not follow the dynamics. Many existing trajectory planning approaches, especially in mobile robotics, encode dynamic aspects directly in the representation (e.g. in the form of regular lattices) which requires a precise knowledge of the environmental and dynamic properties of particular autonomous entities making designing and implementing such trajectory planning approaches quite difficult. The concept of trajectory planning we implemented might not be as precise but the modular architecture makes the design and implementation easier because we can use (modified) well known path planning methods and define models of dynamics of autonomous entities separately. This seems to be appropriate for simulations used in feasibility studies for some complex autonomous systems or in computer games etc. Our basic implementation of the augmented A*, Theta* and Lazy Theta* algorithms is also experimentally evaluated. We compare (i) the augmented and basic A*, Theta* and Lazy Theta* algorithms and (ii) optimizing of augmented Theta* and Lazy Theta* for distance (the trajectory length) and duration (time needed to move through the trajectory).