Propene and 1-Octene Hydroformylation with Silica-Supported, Ionic Liquid-Phase (SILP) Rh-Phosphine Catalysts in Continuous Fixed-Bed Mode

Supported ionic liquid-phase (SILP) catalysts were made by immobilizing Rh-monophosphine complexes of bis(m-phenylguanidinium)phenylphosphine 1 and NORBOS 2 ligands in 1-n-butyl-3-methylimidazolium hexafluorophosphate, [BMIM][PF₆], on a silica support. The catalysts were active in continuous gas- and liquid-phase hydroformylation of propene and 1-octene, exhibiting TOFs up to 88 h^-1 for SILP Rh-2 catalysts, while only low selectivities up to 74% n-aldehyde (n/iso ratio of 2.8) were obtained. This is the first example of continuous fixed-bed liquid-phase hydroformylation using SILP catalysts.     

Creating geometrically robust designs for highly sensitive problems using topology optimization

Resonance and wave-propagation problems are known to be highly sensitive towards parameter variations. This paper discusses topology optimization formulations for creating designs that perform robustly under spatial variations for acoustic cavity problems. For several structural problems, robust topology optimization methods have already proven their worth. However, it is shown that direct application of such methods is not suitable for the acoustic problem under consideration. A new double filter approach is suggested which makes robust optimization for spatial variations possible. Its effect and limitations are discussed. In addition, a known explicit penalization approach is considered for comparison. For near-uniform spatial variations it is shown that highly robust designs can be obtained using the double filter approach. It is finally demonstrated that taking non-uniform variations into account further improves the robustness of the designs.     

Combining the triangle method with thermal inertia to estimate regional evapotranspiration — Applied to MSG-SEVIRI data in the Senegal River basin

Spatially distributed estimates of evaporative fraction and actual evapotranspiration are pursued using a simple remote sensing technique based on a remotely sensed vegetation index (NDVI) and diurnal changes in land surface temperature. The technique, known as the triangle method, is improved by utilizing the high temporal resolution of the geostationary MSG-SEVIRI sensor. With 15 min acquisition intervals, the MSG-SEVIRI data allow for a precise estimation of the morning rise in land surface temperature which is a strong proxy for total daytime sensible heat fluxes. Combining the diurnal change in surface temperature, dT_s with an interpretation of the triangular shaped dT_s − NDVI space allows for a direct estimation of evaporative fraction. The mean daytime energy available for evapotranspiration (R_n − G) is estimated using several remote sensors and limited ancillary data. Finally regional estimates of actual evapotranspiration are made by combining evaporative fraction and available energy estimates. The estimated evaporative fraction (EF) and actual evapotranspiration (ET) for the Senegal River basin have been validated against field observations for the rainy season 2005. The validation results showed low biases and RMSE and R² of 0.13 [−] and 0.63 for EF and RMSE of 41.45 W m^− 2 and R² of 0.66 for ET.     

A divide-and-conquer approach for analysing overlaid data structures

We present a static program analysis for overlaid data structures such that a node in the structure includes links for multiple data structures and these links are intended to be used at the same time. These overlaid data structures are frequently used in systems code, in order to impose multiple types of indexing structures over the same set of nodes. Our analysis implements two main ideas. The first is to run multiple sub-analyses that track information about non-overlaid data structures, such as lists. The second idea is to control the communication among the sub-analyses using ghost states and ghost instructions. The purpose of this control is to achieve a high level of efficiency by allowing only necessary information to be transferred among sub-analyses and at as few program points as possible. Our analysis has been successfully applied to prove the memory safety of the Linux deadline IO scheduler and AFS server.     

Point or pointless – quality of ground data

A method for measuring continuously the inherent optical properties of water together with the salinity and the temperature values was developed and tested at the Division of Geophysics, University of Helsinki. The flow‐through system operates from a moving boat and has been used to collect parameters for optical modelling, coastal zone and inland water studies and to track the path of the fresh water spreading into sea areas. The system was also used in Lake Vänern, Sweden, to calculate the radiance reflectance, R _r, and total back‐scattering efficiency, b _{b eff}, along a transect. An example from the Gulf of Finland is presented, which shows how the spectral behaviour of light changes when moving away from a fresh water source. Data collected from the River Kymijoki estuary showed that the details in a rapidly changing environment were strongly smoothed in a Moderate Resolution Imaging Spectroradiometer (MODIS) data image. The correlation for continuously measured scattering values and MODIS channel 1 data varied between 0.70 and 0.85, n?=?126. Flow‐through measurement datasets can be used to obtain representative sites for vertical profiles or calibration measurements.     

Implicit Contact Handling for Deformable Objects

We present an algorithm for robust and efficient contact handling of deformable objects. By being aware of the internal dynamics of the colliding objects, our algorithm provides smooth rolling and sliding, stable stacking, robust impact handling, and seamless coupling of heterogeneous objects, all in a unified manner. We achieve dynamicsawareness through a constrained dynamics formulation with implicit complementarity constraints, and we present two major contributions that enable an efficient solution of the constrained dynamics problem: a time stepping algorithm that robustly ensures non-penetration and progressively refines the formulation of constrained dynamics, and a new solver for large mixed linear complementarity problems, based on iterative constraint anticipation. We show the application of our algorithm in challenging scenarios such as multi-layered cloth moving at high velocities, or colliding deformable solids simulated with large time steps.     

Analysis of particle cloud height dynamics in a stirred tank

Local and temporal variations of the particle cloud formed in a cylindrical mixing vessel were investigated experimentally. Different particle sizes (0.5, 1, and 2 mm) and volumetric concentration up to 20 vol % were evaluated at different impeller speeds. The time‐averaged cloud height was linear with impeller frequency and with volume concentration. Suspensions with larger particles had a lower average cloud height, while the standard deviation for the temporal cloud height variation was larger. Two strong periodic phenomena were identified to be dominating the particle cloud height variations. The frequencies were linear with impeller speed, resulting in dimensionless frequencies of S₁=0.02–0.03 and S₂=0.05–0.06. The frequencies were affected by neither the particle size nor the volumetric concentration. The amplitude showed no dependency on the particle size, but the S₂ amplitude significantly decreases and S₁ increases with increasing solid concentration. The results were compared to LES/discrete element method simulations and showed a fair agreement. © 2015 American Institute of Chemical Engineers AIChE J, 62: 338–348, 2016     

3- Instead of 4-helix formation in a de novo designed protein in solution revealed by small-angle X-ray scattering

De novo design and chemical synthesis of proteins and their mimics are central approaches for understanding protein folding and accessing proteins with novel functions. We have previously described carbohydrates as templates for the assembly of artificial proteins, so-called carboproteins. Here, we describe the preparation and structural studies of three alpha-helical bundle carboproteins, which were assembled from three different carbohydrate templates and one amphiphilic hexadecapeptide sequence. This heptad repeat peptide sequence has been reported to lead to 4-alpha-helix formation. The low resolution solution structures of the three carboproteins were analyzed by means of small-angle X-ray scattering (SAXS) and synchrotron radiation circular dichroism (SRCD). The ab initio SAXS data analysis revealed that all three carboproteins adopted an unexpected 3+1-helix folding topology in solution, while the free peptide formed a 3-helix bundle. This finding is consistent with the calculated alpha-helicities based on the SRCD data, which are 72 and 68 % for two of the carboproteins. The choice of template did not affect the overall folding topology (that is for the 3+1 helix bundle) the template did have a noticeable impact on the solution structure. This was particularly evident when comparing 4-helix carboprotein monomers with the 2x2-helix carboprotein dimer as the latter adopted a more compact conformation. Furthermore, the clear conformational differences observed between the two 4-helix (3+1) carboproteins based on D-altropyranoside and D-galactopyranoside support the notion that folding is affected by the template, and subtle variations in template distance-geometry design may be exploited to control the solution fold. In addition, the SRCD data show that template assembly significantly increases thermostability.     

Deep Expectation of Real and Apparent Age from a Single Image Without Facial Landmarks

In this paper we propose a deep learning solution to age estimation from a single face image without the use of facial landmarks and introduce the IMDB-WIKI dataset, the largest public dataset of face images with age and gender labels. If the real age estimation research spans over decades, the study of apparent age estimation or the age as perceived by other humans from a face image is a recent endeavor. We tackle both tasks with our convolutional neural networks (CNNs) of VGG-16 architecture which are pre-trained on ImageNet for image classification. We pose the age estimation problem as a deep classification problem followed by a softmax expected value refinement. The key factors of our solution are: deep learned models from large data, robust face alignment, and expected value formulation for age regression. We validate our methods on standard benchmarks and achieve state-of-the-art results for both real and apparent age estimation.