Intradermal and subcutaneous leiomyosarcoma: a clinicopathological and immunohistochemical study of 41 cases

Superficial leiomyosarcomas are rare tumours. The lesions confined to the dermis, contrary to those involving the subcutis, have been reported to carry a favourable prognosis. A retrospective study of 41 consecutive cases of surgically treated intradermal and subcutaneous leiomyosarcomas was undertaken in order to determine the prognostic factors that may influence the survival of these patients. Seven tumours were predominantly intradermal and 34 involved the subcutaneous tissue. Fifty-four percent of the tumours were located in the lower extremities. All cases stained positively for smooth muscle antigen and 66% for desmin. The tumours were classified with regard to tumour grade I (low grade, 3%), II (intermediate, 12%), IIIA (high grade, 46%) and IIIB (high grade, 39%). In all patients, follow-up information was available. Mean follow-up time was 5 years. The patients with intradermal tumours were all alive without signs of recurrence, whereas 14 of those with leiomyosarcomas involving the subcutis have died with pulmonary metastases. Our study confirms that "pure" intradermal leiomyosarcomas independent of tumour grade behave in a benign fashion, probably due to small tumour size. Tumour size > or = 5 cm, deep localization with fascia involvement, and high malignancy grade (IIIB) were found to deteriorate survival based on a univariate analysis. However, in a multivariate analysis only tumour size was found to be an independent prognostic factor.     

On projection methods,convergence and robust formulations in topology optimization

Mesh convergence and manufacturability of topology optimized designs have previously mainly been assured using density or sensitivity based filtering techniques. The drawback of these techniques has been gray transition regions between solid and void parts, but this problem has recently been alleviated using various projection methods. In this paper we show that simple projection methods do not ensure local mesh-convergence and propose a modified robust topology optimization formulation based on erosion, intermediate and dilation projections that ensures both global and local mesh-convergence.     

Attribute selection with fuzzy decision reducts

Rough set theory provides a methodology for data analysis based on the approximation of concepts in information systems. It revolves around the notion of discernibility: the ability to distinguish between objects, based on their attribute values. It allows to infer data dependencies that are useful in the fields of feature selection and decision model construction. In many cases, however, it is more natural, and more effective, to consider a gradual notion of discernibility. Therefore, within the context of fuzzy rough set theory, we present a generalization of the classical rough set framework for data-based attribute selection and reduction using fuzzy tolerance relations. The paper unifies existing work in this direction, and introduces the concept of fuzzy decision reducts, dependent on an increasing attribute subset measure. Experimental results demonstrate the potential of fuzzy decision reducts to discover shorter attribute subsets, leading to decision models with a better coverage and with comparable, or even higher accuracy.     

A boundary value problem for the KdV equation: Comparison of finite-difference and Chebyshev methods

Solutions of a boundary value problem for the Korteweg–de Vries equation are approximated numerically using a finite-difference method, and a collocation method based on Chebyshev polynomials. The performance of the two methods is compared using exact solutions that are exponentially small at the boundaries. The Chebyshev method is found to be more efficient.     

Classifying species of individual trees by intensity and structure features derived from airborne laser scanner data

The objective of this study was to identify candidate features derived from airborne laser scanner (ALS) data suitable to discriminate between coniferous and deciduous tree species. Both features related to structure and intensity were considered. The study was conducted on 197 Norway spruce and 180 birch trees (leaves on conditions) in a boreal forest reserve in Norway. The ALS sensor used was capable of recording multiple echoes. The point density was 6.6 m^− 2. Laser echoes located within the vertical projection of the tree crowns, which were assumed to be circular and defined according to field measurements, were attributed to three categories: “first echoes of many”, “single echoes”, or “last echoes of many echoes”. They were denoted FIRST, SINGLE, and LAST, respectively. In tree species classification using ALS data features should be independent of tree heights. We found that many features were dependent on tree height and that this dependency influenced selection of candidate features. When we accounted for this dependency, it was revealed that FIRST and SINGLE echoes were located higher and LAST echoes lower in the birch crowns than in spruce crowns. The intensity features of the FIRST echoes differed more between species than corresponding features of the other echo categories. For the FIRST echoes the intensity values tended to be higher for birch than spruce. When using the various features for species classification, maximum overall classification accuracies of 77% and 73% were obtained for structural and intensity features, respectively. Combining candidate features related to structure and intensity resulted in an overall classification accuracy of 88%.     

On fully stressed design and <Emphasis Type="Italic">p</Emphasis>-norm measures in structural optimization

This brief note revisits the fully stressed design schemes and p-norm measures used in stress-based structural optimization. Two simple shape optimization cases are used to remind the reader that fully stressed designs only are optimal when unimpeded by geometrical restrictions and that high values of the stress norm are needed in order to achieve satisfactory designs.     

A short numerical study on the optimization methods influence on topology optimization

Structural and Multidisciplinary Optimization - Structural topology optimization problems are commonly defined using continuous design variables combined with material interpolation schemes. One of...     

Redesign of adaptive observers for improved parameter identification in nonlinear systems

We propose a method for redesigning adaptive observers for nonlinear systems. The redesign uses an adaptive law that is based on delayed observers. This increases the computational burden, but gives significantly better parameter identification and robustness properties. In particular, given that a special persistency of excitation condition is satisfied, we prove uniform global asymptotic stability and semi-global exponential stability of the origin of the state and parameter estimation error, and give explicit lower bounds on the convergence rate of both the state and parameter estimation error dynamics. For initial conditions with a known upper bound, we prove tunable exponential convergence rate. To illustrate the use of the proposed method, we apply it to estimate the unmeasured flow rate and the uncertain friction parameters in a model of a managed pressure drilling system. The simulation results clearly show the improved performance of the redesigned adaptive observer compared to a traditional design.     

Miniaturization and integration of photoacoustic detection with amicrofabricated chemical reactor system

Photoacoustic spectroscopy is a useful technique for monitoring chemical composition in mesoscale analysis systems because the detection limit scales favorably with miniaturization. The key element of a photoacoustic spectrometry system is the detector. This work focuses on the miniaturization of photoacoustic detection. In particular, we are using 3.4 μm light to detect propane in a carbon dioxide background-a system that is useful for monitoring combustion reactions. Two systems have been developed. In the first, a miniature photoacoustic cell has been machined into the mounting block of a microfabricated chemical reactor, demonstrating the integration of a photoacoustic detector with a microsystem. The cell used a hearing aid microphone and an infrared diode that was modulated at the first acoustic resonance of the cell. As the gas composition of the cell changed from carbon dioxide to propane the resonance peak was observed to shift and increase, as was expected from theory. This work also presents the first demonstration of a microfabricated photoacoustic detection cell. The cell used an optical microphone and laser excitation brought into the cell via an optical fiber. The light was modulated at a frequency far below the first acoustic resonance, and a signal of 0.05 Pa was observed in the presence of propane     

Multiple Comparisons in Induction Algorithms

A single mechanism is responsible for three pathologies of induction algorithms: attribute selection errors, overfitting, and oversearching. In each pathology, induction algorithms compare multiple items based on scores from an evaluation function and select the item with the maximum score. We call this a multiple comparison procedure (MCP). We analyze the statistical properties of MCPs and show how failure to adjust for these properties leads to the pathologies. We also discuss approaches that can control pathological behavior, including Bonferroni adjustment, randomization testing, and cross-validation.