Co-firing coal with different types of biomass is increasingly being applied in thermal power plants in Europe. The main motive for the use of biomass as the second fuel in coal-fired power plants is the reduction of CO 2 emissions, and related financial benefits in accordance with the relevant international regulations and agreements. Likewise, the application of primary measures in the combustion chamber, which also includes air staging and/or reburning, results in a significant reduction in emission of polluting components of flue gases, in particular NO x emissions. In addition to being efficient and their application to new and future thermoblocks is practically unavoidable, their application and existing conventional combustion chamber does not require significant constructional interventions and is therefore relatively inexpensive. In this work results of experimental research of co-firing coals from Middle Bosnian basin with waste woody biomass are presented. Previously formed fuel test matrix is subjected to pulverized combustion under various temperatures and various technical and technological conditions. First of all it refers to the different mass ratio of fuel components in the mixture, the overall coefficient of excess air and to the application of air staging and/or reburning. Analysis of the emissions of components of the flue gases are presented and discussed. The impact of fuel composition and process temperature on the values of the emissions of components of the flue gas is determined. Additionally, it is shown that other primary measures in the combustion chamber are resulting in more or less positive effects in terms of reducing emissions of certain components of the flue gases into the environment. Thus, for example, the emission of NO x of 989 mg/ measured in conventional combustion, with the simultaneous application of air staging and reburning is reduced to 782 mg/, or by about 21%. The effects of the primary measures applied in the combustion chamber are compared and quantified with regard to conventional combustion of coals from Middle Bosnian basin. Article History : Received: November 5 th 2017; Revised: Januari 6th 2018; Accepted: February 1 st 2018; Available online How to Cite This Article : Hodžic, N., Kazagic, A., and Metovic, S. (2018) Experimental Investigation of Co-Firing of Coal with Woody Biomass in Air Staging and Reburning. International Journal of Renewable Energy Development, 7(1), 1-6. https://doi.org/10.14710/ijred.7.1.1-6

Sleep scoring is used as a diagnostic technique in the diagnosis and treatment of sleep disorders. Automated sleep scoring is crucial, since the large volume of data should be analyzed visually by the sleep specialists which is burdensome, time-consuming tedious, subjective, and error prone. Therefore, automated sleep stage classification is a crucial step in sleep research and sleep disorder diagnosis. In this paper, a robust system, consisting of three modules, is proposed for automated classification of sleep stages from the single-channel electroencephalogram (EEG). In the first module, signals taken from Pz-Oz electrode were denoised using multiscale principal component analysis. In the second module, the most informative features are extracted using discrete wavelet transform (DWT), and then, statistical values of DWT subbands are calculated. In the third module, extracted features were fed into an ensemble classifier, which can be called as rotational support vector machine (RotSVM). The proposed classifier combines advantages of the principal component analysis and SVM to improve classification performances of the traditional SVM. The sensitivity and accuracy values across all subjects were 84.46% and 91.1%, respectively, for the five-stage sleep classification with Cohen’s kappa coefficient of 0.88. Obtained classification performance results indicate that, it is possible to have an efficient sleep monitoring system with a single-channel EEG, and can be used effectively in medical and home-care applications.

Despite substantial research carried out over the last decades, it remains difficult to understand the wide range of pharmacological effects of dopaminergic agents. The dopaminergic system is involved in several neurological disorders, such as Parkinson’s disease and schizophrenia. This complex system features multiple pathways implicated in emotion and cognition, psychomotor functions and endocrine control through activation of G protein-coupled dopamine receptors. This review focuses on the system-wide effects of dopaminergic agents on the multiple biochemical and endocrine pathways, in particular the biomarkers (i.e., indicators of a pharmacological process) that reflect these effects. Dopaminergic treatments developed over the last decades were found to be associated with numerous biochemical pathways in the brain, including the norepinephrine and the kynurenine pathway. Additionally, they have shown to affect peripheral systems, for example the hypothalamus-pituitary-adrenal (HPA) axis. Dopaminergic agents thus have a complex and broad pharmacological profile, rendering drug development challenging. Considering the complex system-wide pharmacological profile of dopaminergic agents, this review underlines the needs for systems pharmacology studies that include: i) proteomics and metabolomics analysis; ii) longitudinal data evaluation and mathematical modeling; iii) pharmacokinetics-based interpretation of drug effects; iv) simultaneous biomarker evaluation in the brain, the cerebrospinal fluid (CSF) and plasma; and v) specific attention to condition-dependent (e.g., disease) pharmacology. Such approach is considered essential to increase our understanding of central nervous system (CNS) drug effects and substantially improve CNS drug development.

In this manuscript, the authors investigate the growth of indium zinc oxide, indium zinc oxide (InZnO, IZO) as a channel material for thin-film transistors. IZO is grown at atmospheric pressure and a high deposition rate using spatial atomic layer deposition (S-ALD). By varying the ratio of diethylzinc and trimethylindium vapor, the In/(In þ Zn) ratio of the film can be accurately tuned in the entire range from zinc oxide to indium oxide. Thin film transistors with an In to Zn ratio of 2:1 show high field-effect mobility—exceeding 30 cm2/V s—and excellent stability. The authors demonstrate large scale integration in the form of 19-stage ring oscillators operating at 110 kHz. These electrical characteristics, in combination with the intrinsic advantages of atomic layer deposition, demonstrate the great potential of S-ALD for future display production.

The usage of Artificial Neural Networks (ANNs) for genome-enabled classifications and establishing genome-phenotype correlations have been investigated more extensively over the past few years. The reason for this is that ANNs are good approximates of complex functions, so classification can be performed without the need for explicitly defined input-output model. This engineering tool can be applied for optimization of existing methods for disease/syndrome classification. Cytogenetic and molecular analyses are the most frequent tests used in prenatal diagnostic for the early detection of Turner, Klinefelter, Patau, Edwards and Down syndrome. These procedures can be lengthy, repetitive; and often employ invasive techniques so a robust automated method for classifying and reporting prenatal diagnostics would greatly help the clinicians with their routine work. The database consisted of data collected from 2500 pregnant woman that came to the Institute of Gynecology, Infertility and Perinatology “Mehmedbasic” for routine antenatal care between January 2000 and December 2016. During first trimester all women were subject to screening test where values of maternal serum pregnancy-associated plasma protein A (PAPP-A) and free beta human chorionic gonadotropin (β-hCG) were measured. Also, fetal nuchal translucency thickness and the presence or absence of the nasal bone was observed using ultrasound. The architectures of linear feedforward and feedback neural networks were investigated for various training data distributions and number of neurons in hidden layer. Feedback neural network architecture out performed feedforward neural network architecture in predictive ability for all five aneuploidy prenatal syndrome classes. Feedforward neural network with 15 neurons in hidden layer achieved classification sensitivity of 92.00%. Classification sensitivity of feedback (Elman’s) neural network was 99.00%. Average accuracy of feedforward neural network was 89.6% and for feedback was 98.8%. The results presented in this paper prove that an expert diagnostic system based on neural networks can be efficiently used for classification of five aneuploidy syndromes, covered with this study, based on first trimester maternal serum screening data, ultrasonographic findings and patient demographics. Developed Expert System proved to be simple, robust, and powerful in properly classifying prenatal aneuploidy syndromes.