Hydrolytic degradation of ionically cross-linked polyphosphazene microspheres

The hydrolytic degradation of gel microspheres based on calcium cross-linked phosphazene polyelectrolytes, poly[di(carboxylatophenoxy)phosphazene] (PCPP) and poly[(carboxylatophenoxy) (glycinato)phosphazene] (PCGPP), was investigated. These microspheres are of importance as carriers in protein and cell encapsulation. Both PCPP and PCGPP ionotropic polyphosphazene hydrogels are degradable in an aqueous environment (pH 7.4, 37°C). The degradation rates can be increased by incorporation of hydrolysis sensitive glycinato groups as the pendant structures in the polymer (PCGPP). Hydrolysis of these polymer hydrogels led to low molecular weight (<1,000 Da) products. The erosion and molecular weight profiles varied also according to the molecular weight of the polyphosphazene constituting the gel beads. Another approach to affect the degradation rates consists of coating microspheres with poly-L -lysine. Ionotropic polyphosphazene hydrogels have potential as biodegradable devices for controlled drug delivery systems. © 1994 John Wiley & Sons, Inc.     

The relationship between strength and the composition and fineness of cement

Data from new experiments and from work published during the past 40 years are subjected to regression analysis to determine the relationship between strength and the composition and fineness of cement. The original authors differed in their opinions on the relative importance of C₃S and C₃A. The controversy is examined against a background of experience with a group of materials that are representative of Portland cement in general, and in terms of an analysis based on a model in which, during the first weeks of hardening, the strength developed by C₃S depends on the proportion of C₃A in the cement.     

Patienten — Daten — Schutz

Patientinnen und Patienten vertrauen sich heutzutage nicht mehr nur ihrem Haus- oder Facharzt, sondern immer h?ufiger auch einer Gro?klinik („Krankenhausmaschine“) an. Schon dort entsteht die Frage, wie die ?rztliche Schweigepflicht oder besser: das Patientengeheimnis, noch aufrechterhalten werden kann. Weitere Risiken drohen dem Patientengeheimnis in der zunehmend vernetzten Verarbeitung von medizinischen Befunden z.B. in Praxisnetzen und auch in der Online-Welt, die zweifellos gro?e Vorteile für die behandlungsbedürftigen Menschen bringen. Sie werfen aber auch die Frage nach der Verantwortung für die Sicherheit, Richtigkeit und Verfügbarkeit dieser Daten auf, die nicht selten lebenswichtig sein k?nnen.     

Structural sensitivity analysis of flexible multibody systems modeled with the floating frame of reference approach using the adjoint variable method

For the efficient analysis and optimization of flexible multibody systems, gradient information is often required. Next to simple and easy-to-implement finite difference approaches, analytical methods, such as the adjoint variable method, have been developed and are now well established for the sensitivity analysis in multibody dynamics. They allow the computation of exact gradients and require normally less computational effort for large-scale problems. In the current work, we apply the adjoint variable method to flexible multibody systems with kinematic loops, which are modeled using the floating frame of reference formulation. Thereby, in order to solve ordinary differential equations only, the equations of motion are brought into minimal form using coordinate partitioning, and the constraint equations at position and velocity level are incorporated in the adjoint dynamics. For testing and illustrative purposes, the procedure is applied to compute the structural gradient for a flexible piston rod of a slider–crank mechanism.     

Broadband dielectric microwave microscopy on micron length scales

We demonstrate that a near-field microwave microscope based on a transmission line resonator allows imaging in a substantially wide range of frequencies, so that the microscope properties approach those of a spatially resolved impedance analyzer. In the case of an electric probe, the broadband imaging can be used in a direct fashion to separate contributions from capacitive and resistive properties of a sample at length scales on the order of one micron. Using a microwave near-field microscope based on a transmission line resonator we imaged the local dielectric properties of a focused ion beam milled structure on a high-dielectric-constant Ba(0.6)Sr(0.4)TiO(3) thin film in the frequency range from 1.3 to 17.4 GHz. The electrostatic approximation breaks down already at frequencies above approximately 10 GHz for the probe geometry used, and a full-wave analysis is necessary to obtain qualitative information from the images.     

A discriminative learning framework with pairwise constraints for video object classification

To deal with the problem of insufficient labeled data in video object classification, one solution is to utilize additional pairwise constraints that indicate the relationship between two examples, i.e., whether these examples belong to the same class or not. In this paper, we propose a discriminative learning approach which can incorporate pairwise constraints into a conventional margin-based learning framework. Different from previous work that usually attempts to learn better distance metrics or estimate the underlying data distribution, the proposed approach can directly model the decision boundary and, thus, require fewer model assumptions. Moreover, the proposed approach can handle both labeled data and pairwise constraints in a unified framework. In this work, we investigate two families of pairwise loss functions, namely, convex and nonconvex pairwise loss functions, and then derive three pairwise learning algorithms by plugging in the hinge loss and the logistic loss functions. The proposed learning algorithms were evaluated using a people identification task on two surveillance video data sets. The experiments demonstrated that the proposed pairwise learning algorithms considerably outperform the baseline classifiers using only labeled data and two other pairwise learning algorithms with the same amount of pairwise constraints.