Interactive multiobjective optimization with NIMBUS for decision making under uncertainty

We propose an interactive method for decision making under uncertainty, where uncertainty is related to the lack of understanding about consequences of actions. Such situations are typical, for example, in design problems, where a decision maker has to make a decision about a design at a certain moment of time even though the actual consequences of this decision can be possibly seen only many years later. To overcome the difficulty of predicting future events when no probabilities of events are available, our method utilizes groupings of objectives or scenarios to capture different types of future events. Each scenario is modeled as a multiobjective optimization problem to represent different and conflicting objectives associated with the scenarios. We utilize the interactive classification-based multiobjective optimization method NIMBUS for assessing the relative optimality of the current solution in different scenarios. This information can be utilized when considering the next step of the overall solution process. Decision making is performed by giving special attention to individual scenarios. We demonstrate our method with an example in portfolio optimization.     

Stochastic linear programming with a distortion risk constraint

Coherent distortion risk measures are applied to capture the possible violation of a restriction in linear optimization problems whose parameters are uncertain. Each risk constraint induces an uncertainty set of coefficients, which is proved to be a weighted-mean trimmed region. Thus, given a sample of the coefficients, an uncertainty set is a convex polytope that can be exactly calculated. We construct an efficient geometrical algorithm to solve stochastic linear programs that have a single distortion risk constraint. The algorithm is available as an R-package. The algorithm’s asymptotic behavior is also investigated, when the sample is i.i.d. from a general probability distribution. Finally, we present some computational experience.     

Reactions Between Silicon and Graphite Substrates at High Temperature: In Situ Observations

Graphite as a refractory material has found wide application in many process steps to produce photovoltaic silicon. In the current study, the melting behavior of silicon in contact with different grades of graphite was investigated. The infiltration of silicon into graphite was found to be highly dependent on the internal structure of the graphite substrate. It was confirmed that the heating history of silicon in contact with a graphite substrate strongly influences the melting behavior, which is likely attributed to a gas–solid reaction that forms SiC at less than the liquidus temperature of silicon and alters the surface properties of the graphite. It was also observed that a concentration of CO greater than 5 pct in the inlet gas leads to SiC formation on the surface of the silicon and severely hinders melting.     

Levitron: multi-scale analysis of stability

The Levitron is a typical system for complex Hamiltonian dynamics. Using accurate integrators numerical stability studies were done, especially with respect to variations of the magnetic moment μ for different initial positions x and z. For large μ, equivalent to stronger magnetic fields, the region of stable trajectories is splitted into two parts, whereas for small μ, only one stability region is observed. A linear ansatz is not sufficient to explain this splitting of regions. Vertical and transverse stability conditions have to be combined to understand this behaviour using a multi-scale ansatz. For different hole sizes in the magnetic base plate, the same behaviour appears. For larger holes one has to use stronger magnetic fields. Physically, the stability limits can be identified as critical gradients (forces) of the underlying potential. In x-direction, the stability boundaries are determined by a maximum x-gradient of the potential, which is allowed to act on the top. This derivative of the potential determines a force acting on the top. If this force in x-direction gets too strong, the top deviates from a stable trajectory and gets unstable.     

Low-temperature synthesis of hexamethylenetetramine-stabilised ZnS nanoparticles and its photocatalytic properties

ZnS nanoparticles have been synthesised at low temperature by short reaction (80°C for 2?h) via wet chemical route. The nanoparticles were stabilised using hexamethylenetetramine (HMTA) as surfactant in aqueous solution. The average particle size calculated from X-ray diffraction studies was about 4?nm with cubic zincblende structure. The presence of HMTA in the synthesised ZnS nanoparticles was confirmed by FTIR studies. A significant blue shift was observed in the optical absorption band edge for the ZnS nanoparticles as compared to the bulk, indicating a strong quantum confinement. The room temperature photoluminescence (PL) spectrum showed a broad green emission peak at around 502?nm. The photocatalytic property of HMTA-stabilised ZnS nanoparticles were investigated on the decolourisation of methylene blue aqueous solution under UV light irradiation.     

Evaluating the impact of task demands and block resolution on the effectiveness of pixel-based visualization

Pixel-based visualization is a popular method of conveying large amounts of numerical data graphically. Application scenarios include business and finance, bioinformatics and remote sensing. In this work, we examined how the usability of such visual representations varied across different tasks and block resolutions. The main stimuli consisted of temporal pixel-based visualization with a white-red color map, simulating monthly temperature variation over a six-year period. In the first study, we included 5 separate tasks to exert different perceptual loads. We found that performance varied considerably as a function of task, ranging from 75% correct in low-load tasks to below 40% in high-load tasks. There was a small but consistent effect of resolution, with the uniform patch improving performance by around 6% relative to higher block resolution. In the second user study, we focused on a high-load task for evaluating month-to-month changes across different regions of the temperature range. We tested both CIE L*u*v* and RGB color spaces. We found that the nature of the change-evaluation errors related directly to the distance between the compared regions in the mapped color space. We were able to reduce such errors by using multiple color bands for the same data range. In a final study, we examined more fully the influence of block resolution on performance, and found block resolution had a limited impact on the effectiveness of pixel-based visualization.     

Quantification of deep hole-trap filling by molecular p-doping: Dependence on the host material purity

The p-doping effect of the fluorinated fullerene C₆₀ F₃₆ doped into organic thin films of N,N,N′,N′-tetrakis(4-methoxyphenyl)-benzidine (MeO-TPD) of different purification grades is systematically investigated by photoemission spectroscopy. By reducing the molar doping ratio to MR = 2.9 × 10^?4, the Fermi-level shift upon doping is resolved in particular at very low doping concentrations. In comparison to four times sublimated MeO-TPD, 5 times more C₆₀F₃₆ molecules have to be doped into unpurified MeO-TPD films to shift the Fermi-level just above its intrinsic position. This finding is discussed in terms of a statistical model, showing that narrow deep hole-trap states are additionally present in the unpurified host material which are hindering an efficient generation of free charge carriers at molar doping ratios below MR = 0.002.