Accessory artificial circulation in complex treatment of experimental acute toxic pulmonary edema

In experiments on 50 dogs with toxic acute edema of the lung, induced with intravenous injection of 0.1% silver nitrate, the authors have studied the efficacy of accessory artificial circulation and "conservative" therapy. During the perfusion a discharge of the right portions, adequate extracorporeal gas metabolism, normalization of blood gas and acid-base balance were noted; an intensity of pulmonary edema is descreased. An intensive therapy for pulmonary edema was found to be more effective in association of "conservative" treatment with venoarterial perfusion and blood oxygenation.     

Chromosome studies in "preleukemia". III. Myelofibrosis

Cytogenetic studies were done on 18 patients with myelofibrosis or the closely related syndrome, undifferentiated myeloproliferative disorder (MPD). Clones of cells with chromosome abnormalities were demonstrated in the blood of eight individuals, including two with a history of radiation therapy and two with "acute myelofibrosis". Trisomy 8 was present in the latter two patients, but otherwise, there was no consistent cytogenetic pattern or correlation with specific hematologic findings. Sixteen of these patients have been followed for more than 1 year or until death; none has progressed to leukemia. The results indicate that chromosome abnormalities are relatively common in this disorder, but as with polycythemia vera, and unlike some other "preleukemic" states, the aberrant clones in myelofibrosis do not appear to indicate that clinical leukemia is imminent.     

Structural optimization of non-linear systems under stochastic excitation

This work concentrates on the structural optimization of a class of non-linear systems with deterministic structural parameters subject to stochastic excitation. The optimization problem is formulated as the minimization of an objective function subject to constraints on the response level. The stochastic response is characterized by its first two statistical moments, which are computed by a statistical equivalent linearization technique. The implicit structural optimization problem is replaced by a sequence of explicit sub-optimization problems. The sub-problems are constructed by using a conservative first-order approximation of the objective and constraint functions. The applicability of the proposed design process is demonstrated in three numerical examples where the methodology is applied to systems with nonlinearity of hardening and hysteretic type. The effects of the nonlinearity on the general performance of the final designs are discussed. At the same time, some engineering implications of the results obtained in this work are addressed.     

Migration of DCE applications into CORBA and SOAP environments

Legacy applications based on the Distributed Computing Environment (DCE) are subject to several significant limitations. As the development and support of DCE wanes, object‐oriented development becomes more desirable, and transmission over HTTP is established as the preferred protocol over the Internet, DCE application managers and developers are pressed to find extensions and alternatives to DCE. This paper briefly discusses several alternative targets for migration of DCE systems, then proceeds to detail, compare, and contrast two preferred candidates: CORBA and SOAP. Although we have found that developing a general migration solution for legacy DCE applications to CORBA or SOAP to be a non‐trivial, long‐term project, developing specific solutions that are based on a general architecture is feasible. Given a short list of reasonable premises, many DCE applications may be ported to technologies such as CORBA and SOAP. Copyright © 2002 John Wiley & Sons, Ltd.     

Generating robust and flexible job shop schedules using genetic algorithms

The problem of finding robust or flexible solutions for scheduling problems is of utmost importance for real-world applications as they operate in dynamic environments. In such environments, it is often necessary to reschedule an existing plan due to failures (e.g., machine breakdowns, sickness of employees, deliveries getting delayed, etc.). Thus, a robust or flexible solution may be more valuable than an optimal solution that does not allow easy modifications. This paper considers the issue of robust and flexible solutions for job shop scheduling problems. A robustness measure is defined and its properties are investigated. Through experiments, it is shown that using a genetic algorithm it is possible to find robust and flexible schedules with a low makespan. These schedules are demonstrated to perform significantly better in rescheduling after a breakdown than ordinary schedules. The rescheduling performance of the schedules generated by minimizing the robustness measure is compared with the performance of another robust scheduling method taken from literature, and found to outperform this method in many cases.     

Thermal distribution during destructive pulses in ESD protection devices using a single-shot two-dimensional interferometric method

Thermal distribution during single destructive electrostatic discharge (ESD) events is investigated in smart power ESD protection devices using a two-dimensional holographic interferometry technique. The hot spot dynamics and the position of destructive current filaments is correlated with the thermal distribution under the nondestructive conditions and with the failure analysis results.     

Rendering Caustics on Non-Lambertian Surfaces

This paper presents a new technique for rendering caustics on non-Lambertian surfaces. The method is based on an extension of the photon map which removes previous restrictions limiting the usage to Lambertian surfaces. We add information about the incoming direction to the photons and this allows us to combine the photon map with arbitrary reflectance functions. By using a cone-filter we improve the quality of the radiance estimate in particular at discontinuities. Furthermore we introduce balancing of the photon map which not only reduces the memory requirements but also significantly reduces the rendering time. We have used the method to render caustics on surfaces with reflectance functions varying from Lambertian to glossy specular.     

Learning a decision maker's utility function from (possibly) inconsistent behavior

When modeling a decision problem using the influence diagram framework, the quantitative part rests on two principal components: probabilities for representing the decision maker's uncertainty about the domain and utilities for representing preferences. Over the last decade, several methods have been developed for learning the probabilities from a database. However, methods for learning the utilities have only received limited attention in the computer science community.

A promising approach for learning a decision maker's utility function is to take outset in the decision maker's observed behavioral patterns, and then find a utility function which (together with a domain model) can explain this behavior. That is, it is assumed that decision maker's preferences are reflected in the behavior. Standard learning algorithms also assume that the decision maker is behavioral consistent, i.e., given a model of the decision problem, there exists a utility function which can account for all the observed behavior. Unfortunately, this assumption is rarely valid in real-world decision problems, and in these situations existing learning methods may only identify a trivial utility function. In this paper we relax this consistency assumption, and propose two algorithms for learning a decision maker's utility function from possibly inconsistent behavior; inconsistent behavior is interpreted as random deviations from an underlying (true) utility function. The main difference between the two algorithms is that the first facilitates a form of batch learning whereas the second focuses on adaptation and is particularly well-suited for scenarios where the DM's preferences change over time. Empirical results demonstrate the tractability of the algorithms, and they also show that the algorithms converge toward the true utility function for even very small sets of observations.