The challenge of predicting spinnability: Investigating benefits of adding lignin to cellulose solutions in air-gap spinning

In this study, the underlying mechanism for improved spinnability when mixing lignin and cellulose in solution was investigated. Co-processing of lignin and cellulose has previously been identified as a potential route for production of inexpensive and bio-based carbon fibers. The molecular order of cellulose contributes to the strength of the fibers and the high carbon content of lignin improves the yield during conversion to carbon fibers. The current work presents an additional benefit of combining lignin and cellulose; solutions that contain both lignin and cellulose could be air-gap spun at substantially higher draw ratios than pure cellulose solutions, that is, lignin improved the spinnability. Fibers were spun from solutions containing different ratios of lignin, from 0 to 70 wt%, and the critical draw ratio was determined at various temperatures of solution. The observations were followed by characterization of the solutions with shear and elongational viscosity and surface tension, but none of these methods could explain the beneficial effect of lignin on the spinnability. However, by measuring the take-up force it was found that lignin seems to stabilize against diameter fluctuations during spinning, and plausible explanations are discussed.     

The Pupal Parasitoid Trichopria drosophilae Is Attracted to the Same Yeast Volatiles as Its Adult Host

Journal of Chemical Ecology - There is increasing evidence that microorganisms, particularly fungi and bacteria, emit volatile compounds that mediate the foraging behaviour of insects and therefore...     

Partition Into Triangles on Bounded Degree Graphs

We consider the Partition Into Triangles problem on bounded degree graphs. We show that this problem is polynomial-time solvable on graphs of maximum degree three by giving a linear-time algorithm. We also show that this problem becomes $\mathcal{NP}$ -complete on graphs of maximum degree four. Moreover, we show that there is no subexponential-time algorithm for this problem on graphs of maximum degree four unless the Exponential-Time Hypothesis fails. However, the Partition Into Triangles problem on graphs of maximum degree at most four is in many cases practically solvable as we give an algorithm for this problem that runs in $\mathcal{O}(1.02220^{n})$ time and linear space.     

Elimination and accumulation of polycyclic aromatic hydrocarbons in urban stormwater wet detention ponds

The concentrations of polycyclic aromatic hydrocarbons (PAHS) in water and sediments of seven wet detention ponds receiving urban stormwater were investigated. The ponds comprised traditional wet detention ponds with a permanent wet volume and a storage volume as well as ponds that were expanded with sand filters and other means to improve the removal of micropollutants. The concentrations of sigmaPAH in the sediments varied between 6 +/- 5 and 2,222 +/- 603 ng g(-1) dry weight (mean +/- standard deviation), and were highest in the ponds with lower pond volume per catchment area and did not clearly reflect different activities in the catchments. In general, the concentrations of PAHS in the sediments decreased from inlet to outlet, especially in the systems with good conditions for sedimentation such as systems with flow perpendicular sand dikes and extensive submerged vegetation. High molecular weight PAHs were predominant in the sediments indicating the pyrogenic origin of the PAHS. There was no correlation between PAH species concentrations in water or sediments and their hydrophobicity (log K(ow)). PAH concentrations in water fluctuated in response to intensity and frequency of rain events, whereas concentrations in the sediments integrated the pollutant load over time. Pond systems expanded with sand filters and other technologies to enhance removal of micropollutants consistently had concentrations of PAHS in the effluents below the detection level.     

From big smartphone data to worldwide research: The Mobile Data Challenge

This paper presents an overview of the Mobile Data Challenge (MDC), a large-scale research initiative aimed at generating innovations around smartphone-based research, as well as community-based evaluation of mobile data analysis methodologies. First, we review the Lausanne Data Collection Campaign (LDCC), an initiative to collect unique longitudinal smartphone dataset for the MDC. Then, we introduce the Open and Dedicated Tracks of the MDC, describe the specific datasets used in each of them, discuss the key design and implementation aspects introduced in order to generate privacy-preserving and scientifically relevant mobile data resources for wider use by the research community, and summarize the main research trends found among the 100+ challenge submissions. We finalize by discussing the main lessons learned from the participation of several hundred researchers worldwide in the MDC Tracks.     

Matrix Interpretations for Proving Termination of Term Rewriting

We present a new method for automatically proving termination of term rewriting. It is based on the well-known idea of interpretation of terms where every rewrite step causes a decrease, but instead of the usual natural numbers we use vectors of natural numbers, ordered by a particular nontotal well-founded ordering. Function symbols are interpreted by linear mappings represented by matrices. This method allows us to prove termination and relative termination. A modification of the latter, in which strict steps are only allowed at the top, turns out to be helpful in combination with the dependency pair transformation. By bounding the dimension and the matrix coefficients, the search problem becomes finite. Our implementation transforms it to a Boolean satisfiability problem (SAT), to be solved by a state-of-the-art SAT solver.     

Tracing pathway activities with kinase inhibitors and reverse phase protein arrays

Controlling aberrant protein kinase activity is a promising strategy for a variety of diseases, particularly cancer. Hence, the development of kinase inhibitors is currently a focal point for pharmaceutical research. In this study we utilize a chip-based reverse phase protein array (RPA) platform for profiling of kinase inhibitors in cell-based assays. In combination with the planar wave-guide technology the assay system has an absolute LOD down to the low zeptomole range. A431 cell lysates were analyzed for the activation state of key effectors in the epidermal growth factor (EGF) and insulin signaling pathways to validate this model for compound screening. A microtiter-plate format for growing, treating, and lysing cells was shown to be suitable for this approach, establishing the value of the technology as a screening tool for characterization of large numbers of kinase inhibitors against a wide variety of cellular signaling pathways. Moreover, the reverse array format allows rapid development of site-specific phosphorylation assays, since in contrast to ELISA type systems only a single antigen-specific antibody is required.     

Solving the Beck and Wieland model with optimal experimentation in DualPC

Currently, there is a renewed interest in the use of optimal experimentation (adaptive control) in economics. Example are found in [Amman and Kendrick, 1999], [Amman and Kendrick, 2003], [Cosimano, in?press], [Cosimano and Gapen, 2005b], [Cosimano and Gapen, 2005a], [Cosimano and Gapen, 2006], [Tesfaselassie et?al., 2007], [Tucci, 1997], [Wieland, 2000a] and [Wieland, 2000b]. In this paper we present the Beck & Wieland model [Beck, G., & Wieland, V. (2002). Learning and control in a changing economic environment. Journal of Economic Dynamics and Control, 26, 1359-1378] and the methodology to solve this model with time-varying parameters using the various control methods described in [Kendrick, 1981] and [Kendrick, 2002]. Furthermore, we also provide numerical results using the DualPC software [Amman, H. M., & Kendrick, D. A. (1999). The DualI/DualPC software for optimal control models: User’s guide. Working paper, Austin, TX 78712, USA: Center for Applied Research in Economics, University of Texas] and show first evidence that optimal experimentation or Dual Control may produce better results than Expected Optimal Feedback.