Brno University of Technology

About: Brno University of Technology is a education organization based out in Brno, Czechia. It is known for research contribution in the topics: Fracture mechanics & Filter (video). The organization has 6339 authors who have published 15226 publications receiving 194088 citations. The organization is also known as: Vysoké učení technické v Brně & BUT.

Methods for carbon nanotubes synthesis—review

Abstract: Carbon nanotubes (CNTs) have been under scientific investigation for more than fifteen years because of their unique properties that predestine them for many potential applications. The field of nanotechnology and nanoscience push their investigation forward to produce CNTs with suitable parameters for future applications. It is evident that new approaches of their synthesis need to be developed and optimized. In this paper we review history, types, structure and especially the different synthesis methods for CNTs preparation including arc discharge, laser ablation and chemical vapour deposition. Moreover, we mention some rarely used ways of arc discharge deposition which involves arc discharge in liquid solutions in contrary to standard used deposition in a gas atmosphere. In addition, the methods for uniform vertically aligned CNTs synthesis using lithographic techniques for catalyst deposition as well as a method utilizing a nanoporous anodized aluminium oxide as a pattern for selective CNTs growth are reported too.

Context dependent recurrent neural network language model

Abstract: Recurrent neural network language models (RNNLMs) have recently demonstrated state-of-the-art performance across a variety of tasks. In this paper, we improve their performance by providing a contextual real-valued input vector in association with each word. This vector is used to convey contextual information about the sentence being modeled. By performing Latent Dirichlet Allocation using a block of preceding text, we achieve a topic-conditioned RNNLM. This approach has the key advantage of avoiding the data fragmentation associated with building multiple topic models on different data subsets. We report perplexity results on the Penn Treebank data, where we achieve a new state-of-the-art. We further apply the model to the Wall Street Journal speech recognition task, where we observe improvements in word-error-rate.

Minimising the present and future plastic waste, energy and environmental footprints related to COVID-19

Abstract: The COVID-19 pandemic has had growing environmental consequences related to plastic use and follow-up waste, but more urgent health issues have far overshadowed the potential impacts. This paper gives a prospective outlook on how the disruption caused by COVID-19 can act as a catalyst for short-term and long-term changes in plastic waste management practices throughout the world. The impact of the pandemic and epidemic following through the life cycles of various plastic products, particularly those needed for personal protection and healthcare, is assessed. The energy and environmental footprints of these product systems have increased rapidly in response to the surge in the number of COVID-19 cases worldwide, while critical hazardous waste management issues are emerging due to the need to ensure destruction of residual pathogens in household and medical waste. The concept of Plastic Waste Footprint (PWF) is proposed to capture the environmental footprint of a plastic product throughout its entire life cycle. Emerging challenges in waste management during and after the pandemic are discussed from the perspective of novel research and environmental policies. The sudden shift in waste composition and quantity highlights the need for a dynamically reponsive waste management system. Six future research directions are suggested to mitigate the potential impacts of the pandemic on waste management systems.

PredictSNP: robust and accurate consensus classifier for prediction of disease-related mutations.

Abstract: Single nucleotide variants represent a prevalent form of genetic variation. Mutations in the coding regions are frequently associated with the development of various genetic diseases. Computational tools for the prediction of the effects of mutations on protein function are very important for analysis of single nucleotide variants and their prioritization for experimental characterization. Many computational tools are already widely employed for this purpose. Unfortunately, their comparison and further improvement is hindered by large overlaps between the training datasets and benchmark datasets, which lead to biased and overly optimistic reported performances. In this study, we have constructed three independent datasets by removing all duplicities, inconsistencies and mutations previously used in the training of evaluated tools. The benchmark dataset containing over 43,000 mutations was employed for the unbiased evaluation of eight established prediction tools: MAPP, nsSNPAnalyzer, PANTHER, PhD-SNP, PolyPhen-1, PolyPhen-2, SIFT and SNAP. The six best performing tools were combined into a consensus classifier PredictSNP, resulting into significantly improved prediction performance, and at the same time returned results for all mutations, confirming that consensus prediction represents an accurate and robust alternative to the predictions delivered by individual tools. A user-friendly web interface enables easy access to all eight prediction tools, the consensus classifier PredictSNP and annotations from the Protein Mutant Database and the UniProt database. The web server and the datasets are freely available to the academic community at http://loschmidt.chemi.muni.cz/predictsnp.