Self-injective algebras and the second Hochschild cohomology group

In this paper we study the second Hochschild cohomology group ${HH}^2(\Lambda)$ of a finite dimensional algebra $\Lambda$. In particular, we determine ${HH}^2(\Lambda)$ where $\Lambda$ is a finite dimensional self-injective algebra of finite representation type over an algebraically closed field $K$ and show that this group is zero for most such $\Lambda$; we give a basis for ${HH}^2(\Lambda)$ in the few cases where it is not zero.Comment: Corrections to some calculation

Fractal analysis of CE CT lung tumours images

AIM The fractal dimension (FD) of a structure provides a measure of its complexity. This pilot study aims to determine FD values for lung cancers visualised on Computed Tomography (CT) and to assess the potential for tumour FD measurements to provide an index of tumour aggression. METHOD Pre-and post-contrast CT images of the thorax acquired from 15 patients with lung cancers of greater than 10mm were transformed to fractal dimension images using a box-counting algorithm at various scales. A region of interest (ROI) was determined covering tumour locations, which were more apparent on FD images as compared to images before processing. The average tumour FD (FDavg) was computed and compared with the intensity average before FD processing. FD values were correlated with 2 markers of tumour aggression: tumour stage and tumour uptake of fluorodeoxyglucose (FDG) as determined by Positron Emission Tomography. RESULTS For pre-contrast images, the tumour FDavg correlated with tumour stage (r = 0.537, p = 0.0387) and FDG uptake (r= 0.64, p< 0.001). FDavg decreased following contrast enhancement for most tumours. CONCLUSION Fractal analysis of CT images of lung tumours could potentially provide additional information about likely tumour aggression and so impact on clinical management decisions and choice of treatment

Texture analysis of aggressive and nonaggressive lung tumor CE CT images

This paper presents the potential for fractal analysis of time sequence contrast-enhanced (CE) computed tomography (CT) images to differentiate between aggressive and nonaggressive malignant lung tumors (i.e., high and low metabolic tumors). The aim is to enhance CT tumor staging prediction accuracy through identifying malignant aggressiveness of lung tumors. As branching of blood vessels can be considered a fractal process, the research examines vascularized tumor regions that exhibit strong fractal characteristics. The analysis is performed after injecting 15 patients with a contrast agent and transforming at least 11 time sequence CE CT images from each patient to the fractal dimension and determining corresponding lacunarity. The fractal texture features were averaged over the tumor region and quantitative classification showed up to 83.3% accuracy in distinction between advanced (aggressive) and early-stage (nonaggressive) malignant tumors. Also, it showed strong correlation with corresponding lung tumor stage and standardized tumor uptake value of fluoro deoxyglucose as determined by positron emission tomography. These results indicate that fractal analysis of time sequence CE CT images of malignant lung tumors could provide additional information about likely tumor aggression that could potentially impact on clinical management decisions in choosing the appropriate treatment procedure

A fractal dimension based optimal wavelet packet analysis technique for classification of meningioma brain tumours

With the heterogeneous nature of tissue texture, using a single resolution approach for optimum classification might not suffice. In contrast, a multiresolution wavelet packet analysis can decompose the input signal into a set of frequency subbands giving the opportunity to characterise the texture at the appropriate frequency channel. An adaptive best bases algorithm for optimal bases selection for meningioma histopathological images is proposed, via applying the fractal dimension (FD) as the bases selection criterion in a tree-structured manner. Thereby, the most significant subband that better identifies texture discontinuities will only be chosen for further decomposition, and its fractal signature would represent the extracted feature vector for classification. The best basis selection using the FD outperformed the energy based selection approaches, achieving an overall classification accuracy of 91.25% as compared to 83.44% and 73.75% for the co-occurrence matrix and energy texture signatures; respectively

Bilgisayar Destekli Dil ÖğReniminden Mobil Destekli Dil ÖğRenimine: Teknolojinin İNgilizce ÖğRetimine Entegrasyonu

Bu çalışma İngilizce öğretiminin teknoloji ile bağdaştırılmasının farklı yönlerine ilişkin genel bir çerçeve çizmektedir. Çalışmada, CALL\u27 dan (Bilgisayar Destekli Dil Öğrenimi) başlayarak MALL (Mobil Destekli Dil Öğrenimi) ve ilgili unsurlar ekseninde, çeşitli yaygın teknolojik enstrümanlar, uygulama ve yaklaşımlar özetlenmektedir. Çalışma genel anlamıyla dil sınıflarını öğretmen merkezli ve kitap güdümlü olmaktan öğrenci merkezli ve daha etkileşimli bir ortama dönüştüren modern bilgi ve iletişim teknolojilerinin İngilizce öğretimini nasıl şekillendirdiğini gözler önüne sermektedir. Bütün bu gelişmelere rağmen, İngilizceyi ikinci ya da yabancı dil olarak öğrenmek için teknolojinin tek başına yeterli olmadığı bu çalışmada ifade edilmiştir. Teknolojinin yabancı dil pedagojisine entegrasyonu, görece sağlam bir kuramsal çerçeveden yoksundur, ve bu konuda kuram ve uygulamanın uyumlu bir şekilde işlemesini sağlamak gerekmektedir. Çalışmanın sonucunda, yenilikçi bir biçimde bilgi ve iletişim teknolojilerini uyarlamada ve eğitim durumlarına en uygun olan seçimleri yapmada asıl sorumluların pedagoglar olduğu ifade edilmiştir

Combined statistical and model based texture features for improved image classification

This paper aims to improve the accuracy of texture classification based on extracting texture features using five different texture measures and classifying the patterns using a naive Bayesian classifier. Three statistical-based and two model-based methods are used to extract texture features from eight different texture images, then their accuracy is ranked after using each method individually and in pairs. The accuracy improved up to 97.01% when model based - Gaussian Markov random field (GMRF) and fractional Brownian motion (fBm) - were used together for classification as compared to the highest achieved using each of the five different methods alone; and proved to be better in classifying as compared to statistical methods. Also, using GMRF with statistical based methods, such as grey level co-occurrence (GLCM) and run-length (RLM) matrices, improved the overall accuracy to 96.94% and 96.55%; respectively