8,124 research outputs found

    KYT2022 Finnish Research Programme on Nuclear Waste Management 2019–2022 : Final Report

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    KYT2022 (Finnish Research Programme on Nuclear Waste Management 2019–2022), organised by the Ministry of Economic Affairs and Employment, was a national research programme with the objective to ensure that the authorities have sufficient levels of nuclear expertise and preparedness that are needed for safety of nuclear waste management. The starting point for public research programs on nuclear safety is that they create the conditions for maintaining the knowledge required for the continued safe and economic use of nuclear energy, developing new know-how and participating in international collaboration. The content of the KYT2022 research programme was composed of nationally important research topics, which are the safety, feasibility and acceptability of nuclear waste management. KYT2022 research programme also functioned as a discussion and information-sharing forum for the authorities, those responsible for nuclear waste management and the research organizations, which helped to make use of the limited research resources. The programme aimed to develop national research infrastructure, ensure the continuing availability of expertise, produce high-level scientific research and increase general knowledge of nuclear waste management

    Examples of works to practice staccato technique in clarinet instrument

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    Klarnetin staccato tekniğini güçlendirme aşamaları eser çalışmalarıyla uygulanmıştır. Staccato geçişlerini hızlandıracak ritim ve nüans çalışmalarına yer verilmiştir. Çalışmanın en önemli amacı sadece staccato çalışması değil parmak-dilin eş zamanlı uyumunun hassasiyeti üzerinde de durulmasıdır. Staccato çalışmalarını daha verimli hale getirmek için eser çalışmasının içinde etüt çalışmasına da yer verilmiştir. Çalışmaların üzerinde titizlikle durulması staccato çalışmasının ilham verici etkisi ile müzikal kimliğe yeni bir boyut kazandırmıştır. Sekiz özgün eser çalışmasının her aşaması anlatılmıştır. Her aşamanın bir sonraki performans ve tekniği güçlendirmesi esas alınmıştır. Bu çalışmada staccato tekniğinin hangi alanlarda kullanıldığı, nasıl sonuçlar elde edildiği bilgisine yer verilmiştir. Notaların parmak ve dil uyumu ile nasıl şekilleneceği ve nasıl bir çalışma disiplini içinde gerçekleşeceği planlanmıştır. Kamış-nota-diyafram-parmak-dil-nüans ve disiplin kavramlarının staccato tekniğinde ayrılmaz bir bütün olduğu saptanmıştır. Araştırmada literatür taraması yapılarak staccato ile ilgili çalışmalar taranmıştır. Tarama sonucunda klarnet tekniğin de kullanılan staccato eser çalışmasının az olduğu tespit edilmiştir. Metot taramasında da etüt çalışmasının daha çok olduğu saptanmıştır. Böylelikle klarnetin staccato tekniğini hızlandırma ve güçlendirme çalışmaları sunulmuştur. Staccato etüt çalışmaları yapılırken, araya eser çalışmasının girmesi beyni rahatlattığı ve istekliliği daha arttırdığı gözlemlenmiştir. Staccato çalışmasını yaparken doğru bir kamış seçimi üzerinde de durulmuştur. Staccato tekniğini doğru çalışmak için doğru bir kamışın dil hızını arttırdığı saptanmıştır. Doğru bir kamış seçimi kamıştan rahat ses çıkmasına bağlıdır. Kamış, dil atma gücünü vermiyorsa daha doğru bir kamış seçiminin yapılması gerekliliği vurgulanmıştır. Staccato çalışmalarında baştan sona bir eseri yorumlamak zor olabilir. Bu açıdan çalışma, verilen müzikal nüanslara uymanın, dil atış performansını rahatlattığını ortaya koymuştur. Gelecek nesillere edinilen bilgi ve birikimlerin aktarılması ve geliştirici olması teşvik edilmiştir. Çıkacak eserlerin nasıl çözüleceği, staccato tekniğinin nasıl üstesinden gelinebileceği anlatılmıştır. Staccato tekniğinin daha kısa sürede çözüme kavuşturulması amaç edinilmiştir. Parmakların yerlerini öğrettiğimiz kadar belleğimize de çalışmaların kaydedilmesi önemlidir. Gösterilen azmin ve sabrın sonucu olarak ortaya çıkan yapıt başarıyı daha da yukarı seviyelere çıkaracaktır

    Machine Learning Research Trends in Africa: A 30 Years Overview with Bibliometric Analysis Review

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    In this paper, a critical bibliometric analysis study is conducted, coupled with an extensive literature survey on recent developments and associated applications in machine learning research with a perspective on Africa. The presented bibliometric analysis study consists of 2761 machine learning-related documents, of which 98% were articles with at least 482 citations published in 903 journals during the past 30 years. Furthermore, the collated documents were retrieved from the Science Citation Index EXPANDED, comprising research publications from 54 African countries between 1993 and 2021. The bibliometric study shows the visualization of the current landscape and future trends in machine learning research and its application to facilitate future collaborative research and knowledge exchange among authors from different research institutions scattered across the African continent

    Investigation of cytotoxic properties of some heterocyclic derivatives by molecular modeling

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    Currently, many technologies have been adopted to boost the efficiency of drugdevelopment and overcome obstacles in the drug discovery pipeline. The application of these approaches spans a wide range, from bioactivity predictions, de novo compound synthesis, target identification to hit discovery, and lead optimization. This dissertation comprises two studies. First, we proposed an original approach based on statistical consideration dedicated to k-means clustering analysis in order to define a set of rules for structural features that would help in designing novel anti-cancer drug candidates. It has been applied successfully to classify 500 cytotoxic compounds with 21 molecular descriptors into distinct clusters. The percentage of molecules in each cluster is 50%, 24.88%, and 25.12% for cluster 1, cluster 2, and cluster 3, respectively. Each cluster groups a homogeneous class of molecules with respect to their molecular descriptors. Silhouette analysis, used as a cluster validation approach proves that the molecules are very well clustered, and there are no molecules placed in the wrong cluster. In silico screening of pharmacological properties ADME and evaluation of drug-likeness were performed for all molecules. The quantitative analysis of molecular electrostatic potential was performed to identify the nucleophilic and electrophilic sites in the representative molecule of each cluster. In addition, a molecular docking study was carried out to investigate the interactions of the paragon molecules with the active binding sites of six different targets. Our findings provide a guide to assist the chemist in selecting and testing only the potential molecules with good pharmacokinetic profiles to improve the clinical outcomes of drug therapies. Second, a simulation-based investigation was conducted to examine the CHK1 inhibitory activity of cytotoxic xanthone derivatives using a hierarchical workflow for molecular docking, MD simulation, ADME-TOX prediction, and MEP analysis. A molecular docking study was conducted for the forty-three xanthone derivatives along with standard Prexasertib into the selected CHK1 protein structures 7AKM and 7AKO. Furthermore, MD studies support molecular docking results and validate the stability of studied complexes in physiological conditions. Moreover, in silico ADME-TOX studies are used to predict the pharmacokinetic, pharmacodynamic, and toxicological properties of the selected eight xanthones and the standard Prexasertib. The quantitative analysis of electrostatic potential was performed for the lead compound L36 to identify the reactive sites and possible noncovalent interactions. Our study provides new unexplored insights into xanthones as CHK1 inhibitors and identified L36 as a potential drug candidate that could undergo further in vivo assays and optimization, laying a solid foundation for the development of CHK1 inhibitors and cancer drug discovery. To the best of our knowledge, this is the first time such a study was conducted for the xanthones with CHK1 by using a computational based approach

    International Conference on Mathematical Analysis and Applications in Science and Engineering – Book of Extended Abstracts

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    The present volume on Mathematical Analysis and Applications in Science and Engineering - Book of Extended Abstracts of the ICMASC’2022 collects the extended abstracts of the talks presented at the International Conference on Mathematical Analysis and Applications in Science and Engineering – ICMA2SC'22 that took place at the beautiful city of Porto, Portugal, in June 27th-June 29th 2022 (3 days). Its aim was to bring together researchers in every discipline of applied mathematics, science, engineering, industry, and technology, to discuss the development of new mathematical models, theories, and applications that contribute to the advancement of scientific knowledge and practice. Authors proposed research in topics including partial and ordinary differential equations, integer and fractional order equations, linear algebra, numerical analysis, operations research, discrete mathematics, optimization, control, probability, computational mathematics, amongst others. The conference was designed to maximize the involvement of all participants and will present the state-of- the-art research and the latest achievements.info:eu-repo/semantics/publishedVersio

    Decomposing scRNA-seq data using topic modelling

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    In recent years, the development of single cell RNA-sequencing technologies has allowed scientists to study heterogeneity of cell populations, compare cells across conditions, analyse biological processes in development and disease, and infer cellular interactions. While single cell studies provide invaluable perspective in understanding disease and identifying therapeutic targets, such datasets are high-dimensional and pose unique challenges compared to earlier technologies. Machine learning techniques have become one of the most popular ways of overcoming those challenges. The work described here develops and applies interpretable models to single cell data. All methods described here are based on topic modelling, a popular technique within natural language processing. In this context, cells correspond to documents and genes to words. Firstly, we investigate the problem of doublet detection and assess the limitations of currently available methods. We propose an alternative approach based on topic modelling. While the proposed approach does not outperform state of the art methods, potential avenues for exploration are highlighted. Next, a topic modelling-based approach is used to detect genes that change as a result of cell-cell interactions in single cells. Experiments using synthetic and real datasets show that our approach is able to detect genes that change as a result of interaction, while also uncovering meaningful biological groups of genes that correspond to the latent topics which aids interpretation. The described approach also alleviates some of the prior information required by the previous methods, in particular ligand-receptor databases, clustering, and generation of synthetic doublets. Finally, the topic model formulation is extended to single cell data ordered in pseudotime. The dynamic topic modelling is able to capture groups of genes that change over time. This dynamic approach outperforms non-temporal topic models and standard differential expression as it detects more biologically relevant groups of genes. The final section outlines potential directions for future research
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