Artificial neural network medical decision support tool: predicting transfusion requirements of ER patients.

Blood product transfusion is a financial concern for hospitals and patients. Efficient utilization of this dwindling resource is a critical problem if hospitals are to maximize patient care while minimizing costs. Traditional statistical models do not perform well in this domain. An additional concern is the speed with which transfusion decisions and planning can be made. Rapid assessment in the emergency room (ER) necessarily limits the amount of usable information available (with respect to independent variables available). This study evaluates the efficacy of using artificial neural networks (ANNs) to predict the transfusion requirements of trauma patients using readily available information. A total of 1016 patient records are used to train and test a backpropagation neural network for predicting the transfusion requirements of these patients during the first 2, 2-6, and 6-24 h, and for total transfusions. Sensitivity and specificity analysis are used along with the mean absolute difference between blood units predicted and units transfused to demonstrate that ANNs can accurately predict most ER patient transfusion requirements, while only using information available at the time of entry into the ER.     

Elongational viscosity of LDPEs and polystyrenes using entrance loss data

For two low‐density polyethylenes and two polystyrenes, axisymmetric and planar elongational viscosities are estimated using entrance loss data from capillary and slit rheometers, respectively. The elongational viscosity is estimated by optimizing the values of various parameters in the Sarkar–Gupta elongational viscosity model such that the entrance loss predicted by a finite element simulation agrees with the corresponding experimental data. The predicted entrance loss is in good agreement with the experimental data at high flow rates. The difference in the experimental and predicted entrance loss at lower flow rates might have been caused by large error in the experimental data in this range. POLYM. ENG. SCI., 2008. © 2007 Society of Plastics Engineers     

Kinetics of thermal decomposition of unsaturated polyester resins with reduced flammability

In this article, the kinetics of thermal decomposition of unsaturated maleic–phthalic polyester resins, flame‐retarded with zinc hydroxystannate, was studied by thermogravimetric analysis at different heating rates. At the first stage, it was found, on the basis of isoconversional analysis by the methods of Friedman and of Ozawa–Flynn–Wall, that the value of the (apparent) activation energy (E) characteristically changes in three steps during the degradation. Further kinetic studies using nonlinear regression methods revealed the best fits for both pristine and stabilized resins. It was observed that the course of E versus the degree of conversion (α) during degradation of zinc hydroxystannate‐containing resins (α > 0.8) was characterized by higher values of E—this phenomenon can be explained in terms of the flame‐retardation action of zinc hydroxystannate, which is believed to operate primarily in the condensed phase. At the next stage, kinetic analysis by the nonregression method was performed to find the kinetic model [f(α) function] of the decomposition process; hence, for pristine resin, the best fit was found for the Avrami–Yerofeeyev model (nuclei growth), and for stabilized samples, the nth‐order function with catalysis proved to be the best approximation. The obtained kinetic parameters in the form of E, the preexponential factor A, and the model function f(α) allow a prediction of the polyester resin's thermal behavior in an extrapolated range of degree of conversion, time, and temperature. © 2003 Wiley Periodicals, Inc. J Appl Polym Sci 88: 2851–2857, 2003     

Subatomic Natural Deduction for a Naturalistic First-Order Language with Non-Primitive Identity

A first-order language with a defined identity predicate is proposed whose apparatus for atomic predication is sensitive to grammatical categories of natural language (e.g., common nouns, verbs, adjectives, adverbs, modifiers). Subatomic natural deduction systems are defined for this naturalistic first-order language. These systems contain subatomic systems which govern the inferential relations which obtain between naturalistic atomic sentences and between their possibly composite components. As a main result it is shown that normal derivations in the defined systems enjoy the subexpression property which subsumes the subformula property with respect to atomic and identity formulae as a special case. The systems admit a proof-theoretic semantics which does not only apply to logically compound but also to atomic and identity formulae—as well as to their components. The potential of the defined systems for a meticulous first-order analysis of natural inferences whose validity crucially depends on expressions of some of the aforementioned categories is demonstrated.     

The COCA quality model for user documentation

According to Pedraz-Delhaes, users evaluate both the product and the vendor on the basis of provided documentation. Thus, a question arises as to what quality characteristics should be taken into account when making a decision about accepting a given user manual. There are some proposals (e.g., ISO Std. 26513 and 26514), but they contain too many quality characteristics and lack orthogonality. The goal of this paper is to propose a simple quality model for user documentation, along with acceptance methods based on it. The model is to be orthogonal and complete. As a result, the COCA quality model is presented, which comprises four orthogonal quality characteristics: Completeness, Operability, Correctness, and Appearance. To check completeness, the proposed quality model has been compared with many other quality models that are directly or indirectly concerned with user documentation. Moreover, two acceptance methods are described in the paper: pure review based on ISO Std. 1028:2008, and documentation evaluation test (a type of browser evaluation test), which is aimed at assessing the operability of user documentation. Initial quality profiles have been empirically collected for both methods—they can be used when interpreting evaluation results obtained for a given user manual.     

Pattern Analysis and Analogy in Shogi: Predicting Shogi Moves from Prior Experience

As a research paradigm, pattern analysis has been shown to be an effective tool for analyzing complex game situations in both chess and go. We extend the prior pattern analysis research in chess to the domain of shogi. Shogi is computationally more complex than chess and should realize greater benefits than the chess domain from pattern recognition and pattern exploitation research. The IAM program, which has accurately predicted up to 28% of the moves for a specific chess player, is redesigned to operate in the domain of shogi. Results similar to those achieved for the domain of chess are achieved in shogi. Received 25 February 1999 / Revised 1 October 1999 / Accepted in revised form 2 February 2000     

A Chamber for Testing the Release of Volatile Substances Secreted by Animals,Especially Mammals,as Exemplified by Substances Released by Rats in Response to Stress

Abstract

The success of the testing of volatile organic compounds emitted by animals is dependent on the creation of appropriate conditions for air sampling subsequently used to assay and identify the compounds. These conditions play a particularly important role in the investigation of pheromones, which are secreted in extremely low concentrations. The authors have not come across any previous work which offers constructional solutions, which would allow avoidance of contamination of the air samples containing volatile substances secreted by animals.

A constructional solution was developed, which provides optimal conditions for their sampling and isolation. Its main advantages are as follows: the exposure chamber (the chamber in which the animal is studied) is filled with synthetic air; the exposure chamber is separated from the atmospheric air with a synthetic air “jacket”; the exposure chamber has been constructed using materials which do not release chemicals and absorb them in trace quantities.