ПЕВЦИ: (АУТОПОЕТИЧКИ) РОМАН БОРИСАВА СТАНКОВИЋА О САМОУКИДАЊУ1

This paper analyses the unfinished novel Pevci (Singers), written by Borisav Stanković, one of the most important Serbian writers of the XX century. Following a short review of the former approaches to this work and a critical reassessment of the novel’s editors’ proceedings, the article offers a semantic analysis of the text. The hero’s attitude towards the norms of patriarchal culture, i.e. the difficulties in establishing a masculine identity, is emphasized as a dominant trait of this novel. Related to this is the protagonist’s feeling of shame and a need for self-annihilation. The paper also discusses poetic similarities with other works of the same author, particularly the novel Gazda Mladen (Master Mladen) and the short story “Oni” (“Them”).У раду се анализира недовршени роман Певци Борисава Станковића, једног од најзначајнијих српских прозаиста ХХ века. Након кратког осврта на досадашње приступе овоме делу и критичког преиспитивања поступака приређивача романа, пружа се семантичка анализа текста. Као доминантно обележје романа истиче се однос јунака према нормама патријархалне културе, тачније проблематика успостављања маскулиног идентитета. С тим у вези у јунаку се јавља осећање стида и потреба за чином самоукидања. Рад разматра и поетичке сродности с другим остварењима истога писца, посебно са романом Газда Младен и причом „Они“

Modeling thermodynamic analysis and simulation of organic rankine cycle using geothermal energy as heat source

In this study, modeling and thermodynamic analysis of an Organic Rankine Cycle using geothermal heat source in Aydın, Turkey has been made and optimum operation conditions were determined by developing simulation software. The model has been validated using an existing study. In consequence, the differences between existing study data and the model seems reasonable close, so the model was verified. Simulation of model has been made by using EES software. In simulation the effect of minimum and maximum pressure and working fluid changes to the system performance has been investigated. ORC using working fluids R141b and R123 and Isopentane has been simulated for selected input values separately. Because of using Isopentane caused geothermal re-injection temperature decrease to inconvenient level, it has not been used as working fluid in simulation. For R141b and R123, maximum and minimum pressure values have been changed from 500 kPa to 3500 kPa and from 100 kPa to 300 kPa, respectively. 2331 kW of net work obtained at 3184 kPa maximum pressure and 2749 kW of net work obtained at 100 kPa minimum pressures for R141b. For R123, 1798 kW of net work obtained at 3184 kPa maximum pressure and 2119 kW of net work obtained at 100 kPa minimum pressures. It has been seen that reducing minimum pressure effects net work more than increasing maximum pressure and R141b has a better performance than R123, especially in the way of net work production.Papers presented to the 12th International Conference on Heat Transfer, Fluid Mechanics and Thermodynamics, Costa de Sol, Spain on 11-13 July 2016

COASTLINE ZONE EXTRACTION USING LANDSAT-8 OLI IMAGERY, CASE STUDY: BODRUM PENINSULA, TURKEY

Coastline extraction is a fundamental work for coastal resource management and coastal environmental protection. Today, by using digital image processing techniques, coastline extraction can be done with remote sensing imagery systems. In this study, Landsat 8 Operational Land Imagery (OLI) data have been the main data source due to free access and sufficient spatial resolution for coast line extraction. This research is focused on determining the coastline length and measuring land area by using Landsat 8 OLI satellite image for Bodrum Peninsula, Turkey. Three commonly used methods have been applied in order to determine sea-land boundary line and its length, and area of the study area. The Automatic Water Extraction Index (AWEI), Iterative Self-Organizing Data Analysis Technique (ISODATA) unsupervised classification technique and on screen digitizing method was chosen for identification of coastal boundaries. Results of coastline length and land areas of Bodrum by using AWEI, ISODATA and on-screen digitizing are compared with each other. This study shows that with using optimal threshold value, AWEI can be used for coast line extraction method with coherently for Landsat 8 OLI satellite imagery. The overall results show that coastline extraction from satellite imagery can be done with sufficient accuracy using spectral water indices instead of time consuming on-screen digitizing

Two types of single-beam deflection and asymmetric transmission in photonic structures without interface corrugations

We study single-beam deflection and asymmetry in transmission, two aspects of the same phenomenon that appear in the topologically simple, nonsymmetric, photonic crystal (PhC)-based structures without corrugations at the interfaces. Strong diffractions enabling efficient blazing, i.e., redistribution of the incident wave energy in favor of the desired higher diffraction order(s), can be achieved owing to the defect-like layer(s) embedded in a regular slab of PhC. The main features, together with the peculiarities of the two basic transmission types and relevant coupling and deflection scenarios, are discussed, for one of which a part of the PhC works in the evanescent-wave regime. Performances are suggested, in which efficient single-beam deflection and asymmetry in transmission can be obtained even when the irregular layer is deeply embedded. More than 97% of the incident wave energy can be converted into a single deflected beam that is associated with the first negative diffraction order, even though the entire structure is nonsymmetric and the diffractive element is located at some distance from the incidence interface. � 2016 Optical Society of America

Automated Prediction of CMEs Using Machine Learning of CME – Flare Associations

YesIn this work, machine learning algorithms are applied to explore the relation between significant flares and their associated CMEs. The NGDC flares catalogue and the SOHO/LASCO CMEs catalogue are processed to associate X and M-class flares with CMEs based on timing information. Automated systems are created to process and associate years of flares and CMEs data, which are later arranged in numerical training vectors and fed to machine learning algorithms to extract the embedded knowledge and provide learning rules that can be used for the automated prediction of CMEs. Different properties are extracted from all the associated (A) and not-associated (NA) flares representing the intensity, flare duration, duration of decline and duration of growth. Cascade Correlation Neural Networks (CCNN) are used in our work. The flare properties are converted to numerical formats that are suitable for CCNN. The CCNN will predict if a certain flare is likely to initiate a CME after input of its properties. Intensive experiments using the Jack-knife techniques are carried out and it is concluded that our system provides an accurate prediction rate of 65.3%. The prediction performance is analysed and recommendation for enhancing the performance are provided

Analysis of the Stability of Openings Excavated in Anisotropic Rocks

Openings excavated in rocks with anisotropic strength are often affected by serious instability, related to slip along the weakness planes. The Jaeger criterion, which is a discontinuous approach, is widely used in the mining and oil and gas industry, because is based on well-known rock strength parameters. However, this model cannot capture features related to the stability of openings drilled in some anisotropic rocks with the combined effect of the in situ state of stress. The Hoek & Brown criterion, adapted to anisotropic rocks, is a continuous criterion that can describe the complex behavior of different types of anisotropy exhibited by rock material. Here we interpreted the results of triaxial tests carried out on a shale and we defined the parameters of the Jaeger criterion and the modified Hoek & Brown criterion. We investigated the stability of boreholes drilled in this shale by varying the in situ state of stress and we compared the results of the two criteria. We found that the Hoek & Brown criterion can appropriately describe the behavior of this shale and can predict more accurately the width of the instability of openings excavated in different conditions

Prediction of Extreme Ultraviolet Variability Experiment (EVE)/Extreme Ultraviolet Spectro-Photometer (ESP) Irradiance from Solar Dynamics Observatory (SDO)/Atmospheric Imaging Assembly (AIA) Images Using Fuzzy Image Processing and Machine Learning

YesThe cadence and resolution of solar images have been increasing dramatically with the launch of new spacecraft such as STEREO and SDO. This increase in data volume provides new opportunities for solar researchers, but the efficient processing and analysis of these data create new challenges. We introduce a fuzzy-based solar feature-detection system in this article. The proposed system processes SDO/AIA images using fuzzy rules to detect coronal holes and active regions. This system is fast and it can handle different size images. It is tested on six months of solar data (1 October 2010 to 31 March 2011) to generate filling factors (ratio of area of solar feature to area of rest of the solar disc) for active regions and coronal holes. These filling factors are then compared to SDO/EVE/ESP irradiance measurements. The correlation between active-region filling factors and irradiance measurements is found to be very high, which has encouraged us to design a time-series prediction system using Radial Basis Function Networks to predict ESP irradiance measurements from our generated filling factors

Solar flare prediction using advanced feature extraction, machine learning and feature selection

YesNovel machine-learning and feature-selection algorithms have been developed to study: (i) the flare prediction capability of magnetic feature (MF) properties generated by the recently developed Solar Monitor Active Region Tracker (SMART); (ii) SMART's MF properties that are most significantly related to flare occurrence. Spatio-temporal association algorithms are developed to associate MFs with flares from April 1996 to December 2010 in order to differentiate flaring and non-flaring MFs and enable the application of machine learning and feature selection algorithms. A machine-learning algorithm is applied to the associated datasets to determine the flare prediction capability of all 21 SMART MF properties. The prediction performance is assessed using standard forecast verification measures and compared with the prediction measures of one of the industry's standard technologies for flare prediction that is also based on machine learning - Automated Solar Activity Prediction (ASAP). The comparison shows that the combination of SMART MFs with machine learning has the potential to achieve more accurate flare prediction than ASAP. Feature selection algorithms are then applied to determine the MF properties that are most related to flare occurrence. It is found that a reduced set of 6 MF properties can achieve a similar degree of prediction accuracy as the full set of 21 SMART MF properties