Secondary extradural spinal tumours in children

Secondary extradural spinal tumours are very rare, but they comprise most of the extradural neoplasms seen in infancy and childhood. Thirty-nine patients with this type of secondary tumour were treated in our hospital between 1965 and 1991. The diagnosis was proved by biopsy in every case. Following surgical decompression, all 35 survivors had radiotherapy and/or chemotherapy. Their outcome is reported.     

Software development with imperfect information

Delivering software systems that fulfill all requirements of the stakeholders is very difficult, if not at all impossible. We consider the problem of coping with imperfect information, like interpreting incomplete requirement specifications or vagueness in decisions, one of the main reasons that makes software design difficult. We define a method for tracing design decisions under imperfect information. To model and compare requirements with estimations, we present fuzzy and stochastic techniques. This approach offers adequate decision support that can deal with imperfect information during software design. The approach is illustrated by a real-world example, based on a storm surge barrier system.     

Forward stage‐wise ensemble regression algorithm to improve base regressors prediction ability: an empirical study

Analysis of scientific data requires accurate regressor algorithms to decrease prediction errors. Lots of machine learning algorithms, that is, neural networks, rule‐based algorithms, regression trees and some kinds of lazy learners, are used to realize this need. In recent years, different ensemble regression strategies were improved to obtain enhanced predictors with lower forecasting errors. Ensemble algorithms combine good models that make errors in different parts of analyzed data. There are mainly two approaches in ensemble regression algorithm generation; boosting and bagging. The aim of this article is to evaluate a boosting‐based ensemble approach, forward stage‐wise additive modelling (FSAM), to improve some widely used base regressors’ prediction ability. We used 10 regression algorithms in four different types to make predictions on 10 diverse data from different scientific areas and we compared the experimental results in terms of correlation coefficient, mean absolute error, and root mean squared error metrics. Furthermore, we made use of scatter plots to demonstrate the effect of ensemble modelling on the prediction accuracies of evaluated algorithms. We empirically obtained that in general FSAM enhances the accuracies of base regressors or it at least maintains the base regressor performance.     

Evaluation of Alfa grass soda lignin as a filler for novolak molding powder

Behavior of Alpha grass soda lignin was investigated for the preparation of novolak molding powders. Thermal analysis of the different investigated formulations indicated that molding powders prepared from Alpha grass soda lignin were more resistant to thermal degradation compared to a standard formulation prepared from wood flour. Limiting oxygen index determined for the different composites confirmed the higher thermal stability of Alpha grass soda lignin composites. Mechanical properties tested either from Shore hardness tests or from impact strength measurements revealed a slight decrease of mechanical properties for composites prepared from Alpha grass soda lignin. All these results showed the possibility to replace imported wood flours by locally available Alpha grass soda lignin to obtain phenolic composites with quite similar properties. © 2005 Wiley Periodicals, Inc. J Appl Polym Sci 97: 1065–1068, 2005     

Synthesis of bulk nanocrystalline HfB<Subscript>2</Subscript> from HfCl<Subscript>4</Subscript>–NaBH<Subscript>4</Subscript>–Mg ternary system

This study reports on the synthesis and consolidation of pure HfB₂ powders starting from HfCl₄–NaBH₄–Mg blends via autoclave processing, annealing and purification followed by pressureless sintering (PS, with 2 wt% Co aid) or spark plasma sintering (SPS). During autoclave reactions conducted at 500 °C for 12 h under autogenic pressure, excess amounts of NaBH₄ were utilized to investigate its effects on the reaction products and mechanism. A subsequent washing (with distilled water), annealing (at 750, 1000 and 1700 °C) and acid leaching (HCl) were applied on the as-synthesized products. Pure HfB₂ powders with an average particle size of 145 nm were obtained after autoclave synthesis in the presence of 200 wt% excess NaBH₄, washing, annealing at 1000 °C for 3 h and 6 M HCl leaching. SPS sample has higher relative density and microhardness values (94.18% and 20.99 GPa, respectively) than those of PS sample (90.14% and 14.85 GPa). Relative wear resistance was improved considerably (8.2 times) by employing SPS technique.     

Vibes: A visual language for specifying behavioral requirements of algorithms

Manually verifying the behavior of software systems with respect to a set of requirements is a time-consuming and error-prone task. If the verification is automatically performed by a model checker however, time can be saved, and errors can be prevented. To be able to use a model checker, requirements need to be specified using a formal language. Although temporal logic languages are frequently used for this purpose, they are neither commonly considered to have sufficient usability, nor always naturally suited for specifying behavioral requirements of algorithms. Such requirements can be naturally specified as regular language recognizers such as deterministic finite accepters, which however suffer from poor evolvability: the necessity to re-compute the recognizer whenever the alphabet of the underlying model changes. In this paper, we present the visual language Vibes that both is naturally suited for specifying behavioral requirements of algorithms, and enables the creation of highly evolvable specifications. Based on our observations from controlled experiments with 23 professional software engineers and 21 M.Sc. computer science students, we evaluate the usability of Vibes in terms of its understandability, learnability, and operability. This evaluation suggests that Vibes is an easy-to-use language.     

Solving decentralized POMDP problems using genetic algorithms

The Decentralized Partially Observable Markov Decision Process (DEC-POMDP) model addresses the multiagent planning problem in partially observable environments. Due to its high computational complexity, in general only very small size problems can be solved exactly and most researchers concentrate on approximate solution algorithms to handle more complex cases. However, many approximate solution techniques can handle large size problems only for small horizons due to their exponential memory requirements for representing the policies and searching the policy space. In this study, we offer an approximate solution algorithm called GA-FSC that uses finite state controllers (FSC) to represent a finite-horizon DEC-POMDP policy and searches the policy space using genetic algorithms. We encode FSCs into chromosomes and we use one exact and one approximate technique to calculate the fitness of the chromosomes. The exact calculation technique helps us to obtain better quality solutions with the cost of more processing time compared to the approximate fitness calculation. Our method is able to replicate the best results reported so far in the literature in most cases and it is also able to extend the reported horizons further in almost all cases when compared to optimal approaches.