Warsaw University of Technology

About: Warsaw University of Technology is a education organization based out in Warsaw, Poland. It is known for research contribution in the topics: Microstructure & Optical fiber. The organization has 14293 authors who have published 34362 publications receiving 492211 citations. The organization is also known as: Warsaw Polytechnic & Politechnika Warszawska.

Metal–Support Interactions between Nanosized Pt and Metal Oxides (WO3 and TiO2) Studied Using X-ray Photoelectron Spectroscopy

Abstract: Platinum nanoparticles have been selectively deposited on composites of titanium oxide-carbon and tungsten oxide-carbon. Selectivity of the deposition made it possible to investigate changes in electronic properties of both platinum and oxide support, induced by the so-called strong metal–support interactions (SMSI). X-ray photoelectron spectroscopy (XPS) was used, and changes in binding energy of Pt 4f, Ti 2p, and W 4f core-levels and Pt 4f peak asymmetry were determined. These parameters allowed us to state the changes in local electron density, when Pt is deposited on oxide support. In all cases the binding energy of the Pt 4f signal for platinum deposited on an oxide support was significantly lower in comparison to samples where Pt was solely supported onto carbon. The increase in Pt 4f XPS signal asymmetry was observed. This suggests an increased electron density on Pt. No electron donor could be identified from the analysis of the oxide supports. To explain the observed data, at least two effects mu...

Measurements of neutrino oscillation in appearance and disappearance channels by the T2K experiment with 6.6E20 protons on target

Abstract: We report on measurements of neutrino oscillation using data from the T2K long-baseline neutrino experiment collected between 2010 and 2013. In an analysis of muon neutrino disappearance alone, we find the following estimates and 68% confidence intervals for the two possible mass hierarchies: Normal Hierarchy: $\sin^2\theta_{23}=0.514^{+0.055}_{-0.056}$ and $\Delta m^2_{32}=(2.51\pm0.10)\times 10^{-3}$ eV$^2$/c$^4$ Inverted Hierarchy: $\sin^2\theta_{23}=0.511\pm0.055$ and $\Delta m^2_{13}=(2.48\pm0.10)\times 10^{-3}$ eV$^2$/c$^4$ The analysis accounts for multi-nucleon mechanisms in neutrino interactions which were found to introduce negligible bias. We describe our first analyses that combine measurements of muon neutrino disappearance and electron neutrino appearance to estimate four oscillation parameters and the mass hierarchy. Frequentist and Bayesian intervals are presented for combinations of these parameters, with and without including recent reactor measurements. At 90% confidence level and including reactor measurements, we exclude the region: $\delta_{CP}=[0.15,0.83]\pi$ for normal hierarchy and $\delta_{CP}=[-0.08,1.09]\pi$ for inverted hierarchy. The T2K and reactor data weakly favor the normal hierarchy with a Bayes Factor of 2.2. The most probable values and 68% 1D credible intervals for the other oscillation parameters, when reactor data are included, are: $\sin^2\theta_{23}=0.528^{+0.055}_{-0.038}$ and $|\Delta m^2_{32}|=(2.51\pm0.11)\times 10^{-3}$ eV$^2$/c$^4$.

Origin and control of high-temperature ferromagnetism in semiconductors.

Abstract: The extensive experimental and computational search for multifunctional materials has resulted in the development of semiconductor and oxide systems, such as (Ga,Mn)N, (Zn,Cr)Te and HfO2, which exhibit surprisingly stable ferromagnetic signatures despite having a small or nominally zero concentration of magnetic elements. Here, we show that the ferromagnetism of (Zn,Cr)Te, and the associated magnetooptical and magnetotransport functionalities, are dominated by the formation of Cr-rich (Zn,Cr)Te metallic nanocrystals embedded in the Cr-poor (Zn,Cr)Te matrix. Importantly, the formation of these nanocrystals can be controlled by manipulating the charge state of the Cr ions during the epitaxy. The findings provide insight into the origin of ferromagnetism in a broad range of semiconductors and oxides, and indicate possible functionalities of these composite systems. Furthermore, they demonstrate a bottom-up method for self-organized nanostructure fabrication that is applicable to any system in which the charge state of a constituent depends on the Fermi-level position in the host semiconductor.

Qualitative Evaluation Methods

Abstract: The qualitative evaluation methods presented in this chapter play a complementary role to the quantitative methods referred to in the previous chapter of this book. The author shows wider aspects regarding qualitative research, in addition to detailed remarks concerning fieldwork observation. In the latter part the chapter deals with logic models supporting two key evaluator challenges: measuring expected and achieved project outcomes and attributing these outcomes to specific project activities, based on the application of theory-based approaches. It is essential in the case of development, public and European projects, nevertheless, that the author should encourage these models to be introduced within the wider scope of different projects, including business-related situations. The end of the chapter focuses on the applications of new qualitative intelligent systems. Such systems should not only support the analysis of available qualitative data, but should also be characterized by machine intelligence, as a result of discovering knowledge from data that can be observed subjectively, suggesting specific decision rules, adapting to a particular project situation and the specific needs of researchers and evaluators.

Conditional value at risk and related linear programming models for portfolio optimization

Abstract: Many risk measures have been recently introduced which (for discrete random variables) result in Linear Programs (LP). While some LP computable risk measures may be viewed as approximations to the variance (e.g., the mean absolute deviation or the Gini’s mean absolute difference), shortfall or quantile risk measures are recently gaining more popularity in various financial applications. In this paper we study LP solvable portfolio optimization models based on extensions of the Conditional Value at Risk (CVaR) measure. The models use multiple CVaR measures thus allowing for more detailed risk aversion modeling. We study both the theoretical properties of the models and their performance on real-life data.