Micro-rods of oxidized pentacene obtained by thermal annealing in air of pentacene thin films

The influence of thermal annealing (in air and nitrogen at ambient pressure) on optical properties of pentacene films, well-known material widely used in organic electronic devices, was studied. Pentacene films, whose thickness varies an order of magnitude (30 – 300 nm) depending on the position on the substrate, were polycrystalline at all thicknesses. Raman and UV-vis absorption spectra depend on the position on film implies changes of the film structure with the thickness. These spectra are not largely affected by annealing if it is not performed in air at temperatures higher than 100°C. Prolonged annealing in air, at temperatures higher than 100°C, leads to formation of nano- and micro-scale rod-shaped structures on film surface. Based on scanning electron microscopy measurements, it is supposed that these structures are crystalline. Their UV-vis absorbance indicates that they are composed of more than one species of oxidized pentacene molecules, including 6,13-pentacenequinone. Further study is necessary to precisely determine composition and structure of micro-rods, as well as the mechanism of their formation.Serbian Ceramic Society Conference ADVANCED CERAMICS AND APPLICATION V New Frontiers in Multifunctional Material Science and Processing Serbian Academy of Sciences and Arts, Knez Mihailova 35 Serbia, Belgrade, 21st-23rd September 201

Decision Forests, Convolutional Networks and the Models in-Between

This paper investigates the connections between two state of the art classifiers: decision forests (DFs, including decision jungles) and convolutional neural networks (CNNs). Decision forests are computationally efficient thanks to their conditional computation property (computation is confined to only a small region of the tree, the nodes along a single branch). CNNs achieve state of the art accuracy, thanks to their representation learning capabilities. We present a systematic analysis of how to fuse conditional computation with representation learning and achieve a continuum of hybrid models with different ratios of accuracy vs. efficiency. We call this new family of hybrid models conditional networks. Conditional networks can be thought of as: i) decision trees augmented with data transformation operators, or ii) CNNs, with block-diagonal sparse weight matrices, and explicit data routing functions. Experimental validation is performed on the common task of image classification on both the CIFAR and Imagenet datasets. Compared to state of the art CNNs, our hybrid models yield the same accuracy with a fraction of the compute cost and much smaller number of parameters

Classification Forests for Semantic Segmentation of Brain Lesions in Multi-channel MRI

International audienceClassification forests, as discussed in Chapter 2, have a series of advantageous properties which make them a very good choice for applications in medical image analysis. Classification forests are inherent multi-label classifiers (which allows for the simultaneous segmentation of different tissues), have good generalization properties (which is important as training data is often scarce in medical applications), and are able to deal with very high-dimensional feature spaces (which allows the use of non-local and context-aware features to describe the input data). In this chapter we demonstrate how classification forests can be used as a basic building block to develop state of the art systems for medical image analysis in two challenging applications. These applications perform the segmentation of two different types of brain lesions based on 3D multi-channel magnetic resonance images (MRI) as input. More specifically, we discuss (1) the segmentation of the individual tissues of high-grade brain tumor lesions, and (2) the segmentation of multiple-sclerosis lesions

Evidence of phonon-assisted tunnelling in electrical conduction through DNA molecules

We propose a phonon-assisted tunnelling model for explanation of conductivity dependence on temperature and temperature-dependent I-V characteristics in deoxyribonucleic acid (DNA) molecules. The capability of this model for explanation of conductivity peculiarities in DNA is illustrated by comparison of the temperature dependent I-V data extracted from some articles with tunnelling rate dependences on temperature and field strength computed according to the phonon-assisted tunnelling theory. PACS Codes: 87.15.-v, 71.38.-k, 73.40.GkComment: 6 pages, 3 figure

Linking institutional context to the community and career embeddedness of skilled migrants: The role of destination- and origin-country identifications

Supplementary Information is available online at: https://link-springer-com.ezproxytest.brunel.ac.uk/article/10.1057/s41267-024-00683-w#Sec140 .Migration is one of the most pressing global issues of our time. However, relatively little is known about the factors and mechanisms that govern the post-migration experiences of skilled migrants. We adopt an acculturation- and social identity-based approach to examine how differences between institutional characteristics in the destination and origin country, as well as migrants’ experiences with formal and informal institutions shape their identification with the destination and origin country and contribute to their community and career embeddedness. Our study of 1709 highly skilled migrants from 48 origin countries in 12 destination countries reveals that the institutional environment migrants encounter provides both sources of opportunity (potential for human development and value-congruent societal practices) and sources of disadvantage (experienced ethnocentrism and downgrading). These contrasting dynamics affect migrants’ destination-country identification, their origin-country identification and, ultimately, their embeddedness in the destination country. Our results have important implications for multinational enterprises and policy makers that can contribute to enhancing skilled migrants’ community and career embeddedness. For example, these actors may nurture a work environment and provide supportive policies that buffer against the institutional sources of disadvantage we identified in this study, while helping migrants to leverage the opportunities available in the destination country.Open access funding provided by Vienna University of Economics and Business (WU).. Sebastian Reiche gratefully acknowledges the support from the Ministerio de Ciencia e Innovación, grant ECO2015-68272-P funded by MCIN/AEI/https://doi-org.ezproxytest.brunel.ac.uk/10.13039/501100011033, and by “ERDF A way of making Europe”, by the European Union. Mila Lazarova acknowledges the support of the Canada Research Chairs Program. Lena Zander would like to gratefully acknowledge Jan Wallanders och Tom Hedelius stiftelse and Tore Browaldhs stiftelse, Sweden, for financially supporting her research project (P17-0117) and the workshop organized at Sigtuna Folkhögskola for the research team in June, 2022. Eric Davoine gratefully acknowledges the NCCR LIVES for their financial support

Careers in context: An international study of career goals as mesostructure between societies' career-related human potential and proactive career behaviour

Careers exist in a societal context that offers both constraints and opportunities for career actors. Whereas most studies focus on proximal individual and/or organisational-level variables, we provide insights into how career goals and behaviours are understood and embedded in the more distal societal context. More specifically, we operationalise societal context using the career-related human potential composite and aim to understand if and why career goals and behaviours vary between countries. Drawing on a model of career structuration and using multilevel mediation modelling, we draw on a survey of 17,986 employees from 27 countries, covering nine of GLOBE's 10 cultural clusters, and national statistical data to examine the relationship between societal context (macrostructure building the career-opportunity structure) and actors' career goals (career mesostructure) and career behaviour (actions). We show that societal context in terms of societies' career-related human potential composite is negatively associated with the importance given to financial achievements as a specific career mesostructure in a society that is positively related to individuals' proactive career behaviour. Our career mesostructure fully mediates the relationship between societal context and individuals' proactive career behaviour. In this way, we expand career theory's scope beyond occupation- and organisation-related factors

Automated brain tumour detection and segmentation using superpixel-based extremely randomized trees in FLAIR MRI

PURPOSE: We propose a fully automated method for detection and segmentation of the abnormal tissue associated with brain tumour (tumour core and oedema) from Fluid- Attenuated Inversion Recovery (FLAIR) Magnetic Resonance Imaging (MRI). METHODS: The method is based on superpixel technique and classification of each superpixel. A number of novel image features including intensity-based, Gabor textons, fractal analysis and curvatures are calculated from each superpixel within the entire brain area in FLAIR MRI to ensure a robust classification. Extremely randomized trees (ERT) classifier is compared with support vector machine (SVM) to classify each superpixel into tumour and non-tumour. RESULTS: The proposed method is evaluated on two datasets: (1) Our own clinical dataset: 19 MRI FLAIR images of patients with gliomas of grade II to IV, and (2) BRATS 2012 dataset: 30 FLAIR images with 10 low-grade and 20 high-grade gliomas. The experimental results demonstrate the high detection and segmentation performance of the proposed method using ERT classifier. For our own cohort, the average detection sensitivity, balanced error rate and the Dice overlap measure for the segmented tumour against the ground truth are 89.48 %, 6 % and 0.91, respectively, while, for the BRATS dataset, the corresponding evaluation results are 88.09 %, 6 % and 0.88, respectively. CONCLUSIONS: This provides a close match to expert delineation across all grades of glioma, leading to a faster and more reproducible method of brain tumour detection and delineation to aid patient management

LINKS: Learning-based multi-source IntegratioN frameworK for Segmentation of infant brain images

Segmentation of infant brain MR images is challenging due to insufficient image quality, severe partial volume effect, and ongoing maturation and myelination processes. In the first year of life, the image contrast between white and gray matters of the infant brain undergoes dramatic changes. In particular, the image contrast is inverted around 6-8 months of age, and the white and gray matter tissues are isointense in both T1- and T2-weighted MR images and thus exhibit the extremely low tissue contrast, which poses significant challenges for automated segmentation. Most previous studies used multi-atlas label fusion strategy, which has the limitation of equally treating the different available image modalities and is often computationally expensive. To cope with these limitations, in this paper, we propose a novel learning-based multi-source integration framework for segmentation of infant brain images. Specifically, we employ the random forest technique to effectively integrate features from multi-source images together for tissue segmentation. Here, the multi-source images include initially only the multi-modality (T1, T2 and FA) images and later also the iteratively estimated and refined tissue probability maps of gray matter, white matter, and cerebrospinal fluid. Experimental results on 119 infants show that the proposed method achieves better performance than other state-of-the-art automated segmentation methods. Further validation was performed on the MICCAI grand challenge and the proposed method was ranked top among all competing methods. Moreover, to alleviate the possible anatomical errors, our method can also be combined with an anatomically-constrained multi-atlas labeling approach for further improving the segmentation accuracy