Seismic hazard reassessment of an existing NPP in Slovenia

A seismic hazard reassessment of the site of an existing nuclear power plant in Slovenia was performed. For probabilistic seismic hazard analysis, the extended basic approach laid out by Cornell in 1968 was applied (Cornell, 1968). The study was based on existing data only. To overcome the lack of data and to handle uncertainties in the data, a multiple model approach was applied. Tectonic interpretations, seismic source determinations and estimates of the uncertainty were made by three independent groups of earth-scientists. The delineation of seismic sources and the estimation of their parameters were defined by the distribution of earthquakes, by fault rupture sizes, and by fault slip rates. The Gutenberg–Richter doubly truncated exponential recurrence relationship was used for magnitude distribution. Lower-bound magnitude was set at 5.0 for all seismic sources. Maximum observed earthquakes and assumed fault rupture sizes were used as the main criteria for the estimation of upper-bound magnitudes of area and fault sources respectively. The activity rates were either based on the catalogue or estimated from the assessed average fault slip rates. Subjective weights were assigned to model alternatives and to source parameter alternatives. The results of the study are presented as seismic hazard curves and spectra.     

Labeling of Symbolic Prosody Breaks for the Slovenian Language

This paper presents the data-driven prediction of word level prosody breaks modelling for the Slovenian language. Automatic learning techniques depend on the construction of a large corpus labeled appropriately. This labeling can be done either automatically, or by hand. While automatic labeling can be less accurate than hand labeling, the latter is very time consuming and, in some cases, inconsistent. Therefore, a new interactive tool for word level prosody labeling (major/minor breaks) is presented together with a new semi-automatic approach for determining prosody breaks. This interactive tool combines the advantages of hand labeling and automatic labeling by achieving a high consistency in labeling and reducing the time needed for hand labeling. The labeled Slovenian corpus has been used to train our phrase break prediction module, implementing a neural network (NN) structure. Experiments for the data-driven prediction of major = minor and major/minor phrase breaks were performed. The prediction accuracy achieved marks state-of-the-art word level prosody breaks prediction for the Slovenian language and is comparable to the prediction accuracy of other approaches in which more complex NN structures (Müller et al., 2000) or other prediction methods (Black and Tailor, 1997) were applied, and a much larger corpus was used for training. The overall prediction accuracy achieved is 94% for major = minor breaks and over 98/92% for major/minor phrase breaks, respectively.     

Mechanical,thermal, and burning properties of viscose fabric composites: Influence of epoxy resin modification

The influence of epoxy resin modification by 3‐aminopropyltriethoxysilane (APTES) on various properties of warp knitted viscose fabric is reported in this study. Dynamic mechanical, impact resistance, flexural, thermal properties, and burning behavior of the epoxy/viscose fabric composites are studied with respect to varying content of silane coupling agent. The results obtained for APTES‐modified epoxy resin based composites reinforced with unmodified viscose fabric composites are compared to unmodified epoxy resin based composites reinforced with APTES‐modified viscose fabric. The dynamic mechanical behavior of the APTES‐modified resin based composites indicates improved interfacial adhesion. The composites prepared from modified epoxy resin exhibited a twofold increase in impact resistance. The improved adhesion between the fiber and modified resin was also visible from the scanning electron microscope analysis of the impact fracture surface. There was less influence of resin modification on the flexural properties of the composites. The 5% APTES modification induced early degradation of composites compared to all other composites. The burning rate of all the composites under study is rated to be satisfactory for use in automotive interior applications. © 2018 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2018 , 135, 46673.     

Annotated news corpora and a lexicon for sentiment analysis in Slovene

In this study, we introduce Slovene web-crawled news corpora with sentiment annotation on three levels of granularity: sentence, paragraph and document levels. We describe the methodology and tools that were required for their construction. The corpora contain more than 250,000 documents with political, business, economic and financial content from five Slovene media resources on the web. More than 10,000 of them were manually annotated as negative, neutral or positive. All corpora are publicly available under a Creative Commons copyright license. We used the annotated documents to construct a Slovene sentiment lexicon, which is the first of its kind for Slovene, and to assess the sentiment classification approaches used. The constructed corpora were also utilised to monitor within-the-document sentiment dynamics, its changes over time and relations with news topics. We show that sentiment is, on average, more explicit at the beginning of documents, and it loses sharpness towards the end of documents.     

Deep node ranking for neuro-symbolic structural node embedding and classification

Network node embedding is an active research subfield of complex network analysis. This paper contributes a novel approach to learning network node embeddings and direct node classification using a node ranking scheme, coupled with an autoencoder-based neural network architecture. The main advantages of the proposed Deep Node Ranking (DNR) algorithm are competitive or better classification performance, significantly higher learning speed and lower space requirements when compared to state-of-the-art approaches on 15 real-life structural node classification benchmarks. It also enables exploration of the relationship between symbolic and the derived sub-symbolic node representations, offering insights into the learned node space structure. To avoid the space complexity bottleneck in a direct node classification setting, DNR, if needed, computes stationary distributions of personalized random walks from given nodes in mini-batches, scaling seamlessly to larger networks. The scaling laws associated with DNR were also investigated by considering 1,488 synthetic Erd?s-Rényi networks, demonstrating its scalability to tens of millions of links.     

液相合成甲醇催化剂活性的研究

The effects of reduction procedure, reaction temperature and composition of feed gas on the activity of a CuO-ZnO-Al2O3 catalyst for liquid phase methanol synthesis were studied. An optimized procedure different from conventional ones was developed to obtain higher activity and better stability of the catalyst. Both CO and CO2 in the feed gas were found to be necessary to maintain the activity of catalyst in the synthesis process. Reaction temperature was limited up to 523K, otherwise the catalyst will be deactivated rapidly. Experimental results show that the catalyst deactivation is caused by sintering and fouling, and the effects of CO and CO2 on the catalyst activity are also investigated. The experimental results indicate that the formation of water in the methanol synthesis is negligible when the feed gas contains both CO and CO2. The mechanism for liquid-phase methanol synthesis was discussed and it differed slightly from that for gas-phase synthesis.