Thermodynamic and Experimental Study of the Mg-Sn-Ag-In Quaternary System

Phase equilibria in the Mg-rich region of the Mg-Sn-Ag ternary system were determined by quenching experiments, differential scanning calorimetry, electron probe micro-analysis, and X-ray diffraction techniques. No ternary compounds were found in the studied isothermal sections. A critical evaluation of the available experimental data and a thermodynamic optimization of the Mg-Sn-Ag-In quaternary system were carried out using the calculation of phase diagrams method. The modified quasichemical model in the pair approximation was used for the liquid solution, which exhibits a high degree of short-range order. The solid phases were modeled with the compound energy formalism. All available and reliable experimental data were reproduced within experimental error limits. A self-consistent thermodynamic database was constructed for the Mg-Sn-Ag-In quaternary system, which can be used as a guide for Mg-based alloys development.     

Liquid phase formation in the system AlN–Al2O3–Y2O3. Part I: Experimental investigations

The liquid phase formation in the system AlN–Al₂O₃–Y₂O₃ was investigated via differential thermal analysis (DTA) combined with thermogravimetry (TG). For this purpose 17 samples covering a broad composition area of the quasi-ternary system were densified and heat-treated to achieve the equilibrium state. Melting temperatures were determined by DTA. SEM, EDX and XRD were used to study the phase assemblages and microstructures formed. The results were compared with thermodynamic calculations.     

Analytical reasoning task reveals limits of social learning in networks

Social learning—by observing and copying others—is a highly successful cultural mechanism for adaptation, outperforming individual information acquisition and experience. Here, we investigate social learning in the context of the uniquely human capacity for reflective, analytical reasoning. A hallmark of the human mind is its ability to engage analytical reasoning, and suppress false associative intuitions. Through a set of laboratory-based network experiments, we find that social learning fails to propagate this cognitive strategy. When people make false intuitive conclusions and are exposed to the analytic output of their peers, they recognize and adopt this correct output. But they fail to engage analytical reasoning in similar subsequent tasks. Thus, humans exhibit an ‘unreflective copying bias’, which limits their social learning to the output, rather than the process, of their peers’ reasoning—even when doing so requires minimal effort and no technical skill. In contrast to much recent work on observation-based social learning, which emphasizes the propagation of successful behaviour through copying, our findings identify a limit on the power of social networks in situations that require analytical reasoning.     

Long Range Self‐Assembly of Polythiophene Breath Figures: Optical and Morphological Characterization

Large‐area, device relevant sized microporous thin films are formed with commercially available polythiophenes by the breath figure technique, a water‐assisted micropatterning method, with such semitransparent thin films exhibiting periodicity and uniformity dictated by the length of the polymer side chain. Compared to drop‐casted thin films, the microporous thin films exhibit increased crystallinity due to stronger packing of the polymer inside the honeycomb frame.     

Development of new dominant selectable markers for the nonconventional yeasts Ogataea polymorpha and Candida famata

Nonconventional yeast Candida famata and Ogataea polymorpha are interesting organisms for basic and applied studies. O. polymorpha is methylotrophic thermotolerant yeast capable of xylose alcoholic fermentation whereas C. famata is capable of riboflavin overproduction. Still, the new tools for molecular research of these species are needed. The aim of this study was to develop the new dominant selective markers for C. famata and O. polymorpha usable in metabolic engineering experiments. In this work, the BSD gene from Aspergillus terreus coding for blasticidin S deaminase, O. polymorpha AUR1 gene required for sphingolipid synthesis and IMH3 gene, which encodes IMP dehydrogenase, were tested as the new dominant selective marker genes. Our results showed that AUR1 and IMH3 genes could be used as dominant selective markers for O. polymorpha with frequencies of transformation of 40 and 20 transformants per microgram of DNA, respectively. The IMH3 gene was successfully used as the marker for construction of O. polymorpha strains with increased ethanol production from xylose due to overexpression of TAL1, TKL1 and AOX1 genes. The BSD gene from A. terreus, conferring resistance to blasticidin, was found to be efficient for selection of C. famata transformants.     

Correction to: Co-sputtering of Al1−xScxN thin films on Pt(111): a characterization by Raman and IR spectroscopies

Journal of Materials Science -     

Team NimbRo at MBZIRC 2017: Fast landing on a moving target and treasure hunting with a team of micro aerial vehicles

The Mohamed Bin Zayed International Robotics Challenge (MBZIRC) 2017 has defined ambitious new benchmarks to advance the state‐of‐the‐art in autonomous operation of ground‐based and flying robots. This study covers our approaches to solve the two challenges that involved micro aerial vehicles (MAV). Challenge 1 required reliable target perception, fast trajectory planning, and stable control of an MAV to land on a moving vehicle. Challenge 3 demanded a team of MAVs to perform a search and transportation task, coined “Treasure Hunt,” which required mission planning and multirobot coordination as well as adaptive control to account for the additional object weight. We describe our base MAV setup and the challenge‐specific extensions, cover the camera‐based perception, explain control and trajectory‐planning in detail, and elaborate on mission planning and team coordination. We evaluated our systems in simulation as well as with real‐robot experiments during the competition in Abu Dhabi. With our system, we—as part of the larger team NimbRo—won the MBZIRC Grand Challenge and achieved a third place in both subchallenges involving flying robots.