Abelian monopoles in finite temperature lattice SU(2) gluodynamics: first study with improved action

The properties of the thermal Abelian color-magnetic monopoles in the maximally Abelian gauge are studied in the deconfinement phase of the lattice SU(2) gluodynamics. To check universality of the monopole properties we employ the tadpole improved Symanzik action. The simulated annealing algorithm combined with multiple gauge copies is applied for fixing the maximally Abelian gauge to avoid effects of Gribov copies. We compute the density, interaction parameters, thermal mass and chemical potential of the thermal Abelian monopoles in the temperature range between Tc and 3Tc. In comparison with earlier findings our results for these quantities are improved either with respect to effects of Gribov copies or with respect to lattice artifacts.Comment: 11 pages, 14 figures, 5 tables; substantially changed version, title change

Surface acoustic wave propagation in graphene film

Surface acoustic wave (SAW) propagation in a graphene film on the surface of piezoelectric crystals was studied at the BESSY II synchrotron radiation source. Talbot effect enabled the visualization of the SAW propagation on the crystal surface with the graphene film in a real time mode, and high-resolution x-ray diffraction permitted the determination of the SAW amplitude in the graphene/piezoelectric crystal system. The influence of the SAW on the electrical properties of the graphene film was examined. It was shown that the changing of the SAW amplitude enables controlling the magnitude and direction of current in graphene film on the surface of piezoelectric crystals

Визначення закономірностей побудови гармонійного форменого одягу провідників Укрзалізниці

They have researched arrangements of viewing and emblematic recognizing of stylish patterns of the mode of departmental uniform for conductors of Ukrainian railway from the viewpoint of “human – uniform - surroundings”. They have researched arrangements of sketch presentation of artistic image and structural- geometric intermutation of models for the purpose of creationg typical fashion on the base of additive lines of “golden section”. It is proposed to use banks as independent structural units of project forms for definite geometrical laws and harmonical rations.Исследованы механизмы визуализации и выявления знаковых структур стилистического образа ведомственной спецодеждыпроводников железнолорожного транспорта с позиции системы «человек-спецодежда-среда». Исследованы механизмы эскизного представления художественного образа и структурно-геометрического формообразования моделей с целью создания типизованых форм на основе адитивных рядов «золотого сечения». Предложено использование модулей в качестве самостоятельных структурных единиц проектной формы по определенным геометрическими законами и гармоническими соотношениями.Досліджено механізми  структур стилістичного образу одягу провідників залізничного транспорту з позицій системи «людина-одяг-середовище». Досліджено механізми  представлення образу та структурно-геометричного утворення моделей з метою створення типізованих форм на основі адитивних рядів. Запропоновано застосування  в якості самостійних структурних одиниць проектної форми за визначеними геометричними законами і гармонійними співвідношеннями

Prediction of stroke using deep learning model

© Springer International Publishing AG 2017. Many predictive techniques have been widely applied in clinical decision making such as predicting occurrence of a disease or diagnosis, evaluating prognosis or outcome of diseases and assisting clinicians to recommend treatment of diseases. However, the conventional predictive models or techniques are still not effective enough in capturing the underlying knowledge because it is incapable of simulating the complexity on feature representation of the medical problem domains. This research reports predictive analytical techniques for stroke using deep learning model applied on heart disease dataset. The atrial fibrillation symptoms in heart patients are a major risk factor of stroke and share common variables to predict stroke. The outcomes of this research are more accurate than medical scoring systems currently in use for warning heart patients if they are likely to develop stroke

A Study of Machine Learning Techniques for Daily Solar Energy Forecasting using Numerical Weather Models

Proceedings of: 8th International Symposium on Intelligent Distributed Computing (IDC'2014). Madrid, September 3-5, 2014Forecasting solar energy is becoming an important issue in the context of renewable energy sources and Machine Learning Algorithms play an important rule in this field. The prediction of solar energy can be addressed as a time series prediction problem using historical data. Also, solar energy forecasting can be derived from numerical weather prediction models (NWP). Our interest is focused on the latter approach.We focus on the problem of predicting solar energy from NWP computed from GEFS, the Global Ensemble Forecast System, which predicts meteorological variables for points in a grid. In this context, it can be useful to know how prediction accuracy improves depending on the number of grid nodes used as input for the machine learning techniques. However, using the variables from a large number of grid nodes can result in many attributes which might degrade the generalization performance of the learning algorithms. In this paper both issues are studied using data supplied by Kaggle for the State of Oklahoma comparing Support Vector Machines and Gradient Boosted Regression. Also, three different feature selection methods have been tested: Linear Correlation, the ReliefF algorithm and, a new method based on local information analysis.Publicad

Kharkiv incoherent scatter facility

The structure, parameters and operating modes of the incoherent scatter radar of the Institute of Ionosphere, Kharkiv are presented. Some results of the ionosphere research obtained by this facility are shown.Наведено структуру, параметри і режими роботи радара некогерентного розсіяння Інституту іоносфери (м. Харків). Показано деякі результати спостережень іоносфери за допомогою цього обладнання

Using an Indoor Localization System for Activity Recognition

Recognizing the activity performed by users is importantin many application domains, from e-health to home automation. Thispaper explores the use of a fine-grained indoor localization system, basedon ultra-wideband, for activity recognition. The user is supposed to weara number of active tags. The position of active tags is first determinedwith respect to the space where the user is moving, then some position-independent metrics are estimated and given as input to a previouslytrained system. Experimental results show that accuracy values as highas∼95% can be obtained when using a personalized model

GTP-dependent structural rearrangement of the eRF1:eRF3 complex and eRF3 sequence motifs essential for PABP binding

Translation termination in eukaryotes is governed by the concerted action of eRF1 and eRF3 factors. eRF1 recognizes the stop codon in the A site of the ribosome and promotes nascent peptide chain release, and the GTPase eRF3 facilitates this peptide release via its interaction with eRF1. In addition to its role in termination, eRF3 is involved in normal and nonsense-mediated mRNA decay through its association with cytoplasmic poly(A)-binding protein (PABP) via PAM2-1 and PAM2-2 motifs in the N-terminal domain of eRF3. We have studied complex formation between full-length eRF3 and its ligands (GDP, GTP, eRF1 and PABP) using isothermal titration calorimetry, demonstrating formation of the eRF1:eRF3:PABP:GTP complex. Analysis of the temperature dependence of eRF3 interactions with G nucleotides reveals major structural rearrangements accompanying formation of the eRF1:eRF3:GTP complex. This is in contrast to eRF1:eRF3:GDP complex formation, where no such rearrangements were detected. Thus, our results agree with the established active role of GTP in promoting translation termination. Through point mutagenesis of PAM2-1 and PAM2-2 motifs in eRF3, we demonstrate that PAM2-2, but not PAM2-1 is indispensible for eRF3:PABP complex formation

Data Mining Approaches to Diffuse Large B–Cell Lymphoma Gene Expression Data Interpretation

This paper presents a comprehensive study of gene expression patterns originating from a diffuse large B–cell lymphoma (DLBCL) database. It focuses on the implementation of feature selection and classification techniques. Thus, it firstly tackles the identification of relevant genes for the prediction of DLBCL types. It also allows the determination of key biomarkers to differentiate two subtypes of DLBCL samples: Activated B–Like and Germinal Centre B–Like DLBCL. Decision trees provide knowledge–based models to predict types and subtypes of DLBCL. This research suggests that the data may be insufficient to accurately predict DLBCL types or even detect functionally relevant genes. However, these methods represent reliable and understandable tools to start thinking about possible interesting non–linear interdependencies

An experiment with association rules and classification: post-bagging and conviction

In this paper we study a new technique we call post-bagging, which consists in resampling parts of a classification model rather then the data. We do this with a particular kind of model: large sets of classification association rules, and in combination with ordinary best rule and weighted voting approaches. We empirically evaluate the effects of the technique in terms of classification accuracy. We also discuss the predictive power of different metrics used for association rule mining, such as confidence, lift, conviction and X². We conclude that, for the described experimental conditions, post-bagging improves classification results and that the best metric is conviction.Programa de Financiamento Plurianual de Unidades de I & D.Comunidade Europeia (CE). Fundo Europeu de Desenvolvimento Regional (FEDER).Fundação para a Ciência e a Tecnologia (FCT) - POSI/SRI/39630/2001/Class Project