Rigorous and approximate analysis of metallic gratings in soft X-ray and EUV regions

Abstract

In deep ultraviolet and soft X-ray regions the complex refractive indices of metals differ substantially from their values in the visible region of the spectrum. The implications of this fact are analysed for metallic gratings illuminated by electromagnetic fields with wavelengths ranging from soft X‐rays to near infrared, concentrating on short wavelengths. In particular, we study metal-stripe gratings (linear polarizers for visible light) by rigorous diffraction theory to determine the short-wavelength region in which a combination of the geometrical thin-element approximation and the theory of single-layer films can be applied. Then we study inductive grid filters for protection of X-ray detectors from infrared radiation in space applications.     

Triplex Formation by Oligonucleotides Containing Organomercurated Base Moieties

Homothymine oligonucleotides with a single 5‐mercuricytosine or 5‐mercuriuracil residue at their termini have been synthesized and their capacity to form triplexes has been examined with an extensive array of double‐helical targets. UV and circular dichroism (CD) melting experiments revealed the formation and thermal denaturation of pyrimidine ? purine*pyrimidine‐type triple helices with all oligonucleotide combinations studied. Nearly all triplexes were destabilized upon mercuration of the 3′‐terminal residue of the triplex‐forming oligonucleotide, in all likelihood due to competing intramolecular Hg^II‐mediated base pairing. Two exceptions from this general pattern were, however, observed: 5‐mercuricytosine was stabilizing when placed opposite to a T ? A or A ? T base pair. The stabilization was further amplified in the presence of 2‐mercaptoethanol (but not hexanethiol, thiophenol or cysteine), suggesting a stabilizing interaction other than Hg^II‐mediated base pairing.     

A Self-Calibrated Pipeline ADC with 200 MHz IF-Sampling Frontend

A 13-bit, 50-MS/s pipeline ADC with IF-sampling capability is presented. A high sampling linearity is obtained through the use of bootstrapped switches. A digital self-calibration algorithm with modified capacitor measurement scheme is employed to improve the accuracy of the first two pipeline stages. The prototype, implemented with a 0.35-m BiCMOS (SiGe) technology, shows a 76.5-dB SFDR at a 194.2-MHz signal frequency and dissipates 715 mW power from a 2.9-V supply.     

Simulation of light propagation by local spherical interface approximation

A new local elementary interface approximation is introduced for the modeling of wave propagation through interfaces between homogeneous media. The incident wave and the surface profile are approximated locally by a spherical wave and a spherical surface, respectively. The wave field travels through the modulated structure according to the laws of geometrical optics, being refracted by the surface and propagating to the output plane locally as a geometric spherical wave. Diffraction theory is applied to propagate the field from the output plane onwards. We provide comparisons of the method with the thin-element approximation, the local plane-wave and interface approach, and rigorous diffraction theory using a sinusoidal surface-relief grating as an example. We illustrate the power of the new method by applying it to the analysis of a diffractive beam splitter.     

Aggregation‐Prone Amyloid‐β⋅CuII Species Formed on the Millisecond Timescale under Mildly Acidic Conditions

Metal ions and their interaction with the amyloid beta (Aβ) peptide might be key elements in the development of Alzheimer's disease. In this work the effect of Cu^II on the aggregation of Aβ is explored on a timescale from milliseconds to days, both at physiological pH and under mildly acidic conditions, by using stopped‐flow kinetic measurements (fluorescence and light‐scattering), ¹H NMR relaxation and ThT fluorescence. A minimal reaction model that relates the initial Cu^II binding and Aβ folding with downstream aggregation is presented. We demonstrate that a highly aggregation prone Aβ ? Cu^II species is formed on the sub‐second timescale at mildly acidic pH. This observation might be central to the molecular origin of the known detrimental effect of acidosis in Alzheimer's disease.     

Framework for developing image-based dirt particle classifiers for dry pulp sheets

One important aspect of assessing the quality in pulp and papermaking is dirt particle counting and classification. Knowing the number and types of dirt particles present in pulp is useful for detecting problems in the production process as early as possible and for fixing them. Since manual quality control is a time-consuming and laborious task, the problem calls for an automated solution using machine vision techniques. However, the ground truth required to train an automated system is difficult to ascertain, since all of the dirt particles should be manually segmented and classified based on image information. This paper proposes a framework for developing and tuning dirt particle detection and classification systems. To avoid manual annotation, dry pulp sheets with a single dirt type in each were exploited to generate semisynthetic images with the ground truth information. To classify the dirt particles, a set of features were computed for each image segment. Sequential feature selection was employed to determine a close-to-optimal set of features to be used in classification. The framework was tested both with semisynthetically generated images based on real pulp sheets and with independent original real pulp sheets without any generation. The results of the experiments show that the semisynthetic procedure does not significantly change the properties of images and has little effect on the particle segmentation. The feature selection proved to be important when the number of dirt classes changes since it allows to improve the classification results. Using the standard classification methods, it is possible to obtain satisfactory results, although the methods modeling the data, such as the Bayesian classifier using the Gaussian Mixture Model, show better performance.     

Computing optimal outcomes under an expressive representation of settings with externalities

When a decision must be made based on the preferences of multiple agents, and the space of possible outcomes is combinatorial in nature, it becomes necessary to think about how preferences should be represented, and how this affects the complexity of finding an optimal (or at least a good) outcome. We study settings with externalities, where each agent controls one or more variables, and how these variables are set affects not only the agent herself, but also potentially the other agents. For example, one agent may decide to reduce her pollution, which will come at a cost to herself, but will result in a benefit for all other agents. We formalize how to represent such domains and show that in a number of key special cases, it is NP-complete to determine whether there exists a nontrivial feasible solution (and therefore the maximum social welfare is completely inapproximable). However, for one important special case, we give an algorithm that converges to the solution with the maximal concession by each agent (in a linear number of rounds for utility functions that additively decompose into piecewise constant functions). Maximizing social welfare, however, remains NP-hard even in this setting. We also demonstrate a special case that can be solved in polynomial time using linear programming.     

Identifying,framing and managing uncertainties in project portfolios

Uncertainties in the organization, external environment and from single projects may hamper project portfolio performance unless managed properly. This paper introduces a framework on uncertainties and their management in project portfolios and pursues increased understanding on how managers can take uncertainty into account better. We explore uncertainties, how managers frame them as opportunities or threats, and the actual practice of managing them across ten R&D project portfolios. The framework on project portfolio uncertainties and their management is further refined based on the empirical results. As key contributions, we show evidence on the balanced existence of three types of uncertainties, the threat bias in their framing, and the dominance of rational, opportunity driven mechanisms of control in uncertainty management. We discuss the context-dependent practice of project portfolio management and the need to complement rational mechanisms with structural and cultural, for project portfolio management to become a dynamic capability.     

High-precision diode-laser-based temperature measurement for air refractive index compensation

We present a laser-based system to measure the refractive index of air over a long path length. In optical distance measurements, it is essential to know the refractive index of air with high accuracy. Commonly, the refractive index of air is calculated from the properties of the ambient air using either Ciddor or Edlén equations, where the dominant uncertainty component is in most cases the air temperature. The method developed in this work utilizes direct absorption spectroscopy of oxygen to measure the average temperature of air and of water vapor to measure relative humidity. The method allows measurement of temperature and humidity over the same beam path as in optical distance measurement, providing spatially well-matching data. Indoor and outdoor measurements demonstrate the effectiveness of the method. In particular, we demonstrate an effective compensation of the refractive index of air in an interferometric length measurement at a time-variant and spatially nonhomogeneous temperature over a long time period. Further, we were able to demonstrate 7 mK RMS noise over a 67 m path length using a 120 s sample time. To our knowledge, this is the best temperature precision reported for a spectroscopic temperature measurement.