Ispitivanje zavarljivosti i osjetljivosti mikrolegiranog čelika na pojavu lamelarnih pukotina

In this work are given the testing results of mechanical properties welded joints and microstructure of micro alloyed steel as well as its sensitivity to lamellar cracks appearance. The obtained results show that steel has good resistance to lamellar cracks appearance and with an appropriate wire choice for welding, a good combination of mechanical properties could be obtained at room (ambience) temperatures as well as at low temperatures.U radu su dani rezultati ispitivanja mehaničkih svojstava i mikrostrukture zavarenih spojeva mikrolegiranog čelika, kao i njegove osjetljivosti prema pojavi lamelarnih pukotina. Dobiveni rezultati ukazuju da čelik ima dobru otpornost prema pojavi lamelarnih pukotina i da se, izborom adekvatnih elektroda za zavarivanje, može dobiti dobra kombinacija mehaničkih svojstava, kako na sobnoj, tako i na niskim temperaturama

Simple and Low-cost Fiber-optic Sensors for Detection of UV Radiation

In this paper two simple and low-cost fiberoptic sensors for detection of UV radiation are presented. A U-shaped sensor covered with an UV marker for UV radiation detection and a fiber-optic sensor with one end covered with powder from a mercury lamp are produced and described in details. Both sensors are made of large-core PMMA plastic optical fibers. As UV sources, a solar simulator and four different UV lamps are used. The light spectrum on the fiber output is measured by using an USB spectrometer. Dependence of output light intensity on the distance of end-type sensor with powder from a mercury lamp from UV lamp is investigated as well. On the output of the sensor covered with powder from a mercury lamp are obtained peaks of fluorescent emission at approximately 616 nm and 620 nm wavelengths

Simplified Method to Predict Mutual Interactions of Human Transcription Factors Based on Their Primary Structure

Background: Physical interactions between transcription factors (TFs) are necessary for forming regulatory protein complexes and thus play a crucial role in gene regulation. Currently, knowledge about the mechanisms of these TF interactions is incomplete and the number of known TF interactions is limited. Computational prediction of such interactions can help identify potential new TF interactions as well as contribute to better understanding the complex machinery involved in gene regulation. Methodology: We propose here such a method for the prediction of TF interactions. The method uses only the primary sequence information of the interacting TFs, resulting in a much greater simplicity of the prediction algorithm. Through an advanced feature selection process, we determined a subset of 97 model features that constitute the optimized model in the subset we considered. The model, based on quadratic discriminant analysis, achieves a prediction accuracy of 85.39 % on a blind set of interactions. This result is achieved despite the selection for the negative data set of only those TF from the same type of proteins, i.e. TFs that function in the same cellular compartment (nucleus) and in the same type of molecular process (transcription initiation). Such selection poses significant challenges for developing models with high specificity, but at the same time better reflects real-world problems. Conclusions: The performance of our predictor compares well to those of much more complex approaches for predicting TF and general protein-protein interactions, particularly when taking the reduced complexity of model utilisation into account

Probabilistic Inference of Transcription Factor Binding from Multiple Data Sources

An important problem in molecular biology is to build a complete understanding of transcriptional regulatory processes in the cell. We have developed a flexible, probabilistic framework to predict TF binding from multiple data sources that differs from the standard hypothesis testing (scanning) methods in several ways. Our probabilistic modeling framework estimates the probability of binding and, thus, naturally reflects our degree of belief in binding. Probabilistic modeling also allows for easy and systematic integration of our binding predictions into other probabilistic modeling methods, such as expression-based gene network inference. The method answers the question of whether the whole analyzed promoter has a binding site, but can also be extended to estimate the binding probability at each nucleotide position. Further, we introduce an extension to model combinatorial regulation by several TFs. Most importantly, the proposed methods can make principled probabilistic inference from multiple evidence sources, such as, multiple statistical models (motifs) of the TFs, evolutionary conservation, regulatory potential, CpG islands, nucleosome positioning, DNase hypersensitive sites, ChIP-chip binding segments and other (prior) sequence-based biological knowledge. We developed both a likelihood and a Bayesian method, where the latter is implemented with a Markov chain Monte Carlo algorithm. Results on a carefully constructed test set from the mouse genome demonstrate that principled data fusion can significantly improve the performance of TF binding prediction methods. We also applied the probabilistic modeling framework to all promoters in the mouse genome and the results indicate a sparse connectivity between transcriptional regulators and their target promoters. To facilitate analysis of other sequences and additional data, we have developed an on-line web tool, ProbTF, which implements our probabilistic TF binding prediction method using multiple data sources. Test data set, a web tool, source codes and supplementary data are available at: http://www.probtf.org

Micro alloyed steel weldability and sensibility testing on the lamellar cracks appearance

In this work are given the testing results of mechanical properties welded joints and microstructure of micro alloyed steel as well as its sensitivity to lamellar cracks appearance. The obtained results show that steel has good resistance to lamellar cracks appearance and with an appropriate wire choice for welding, a good combination of mechanical properties could be obtained at room (ambience) temperatures as well as at low temperatures

Optimization of arsenite adsorption on hydroxy apatite based adsorbent using the adaptive neuro-fuzzy inference system / Оптимизация адсорбции арсенита на адсорбенте гидроксиапатита с использованием адаптивной нейро-нечеткой инференционной системы / Optimizacija adsorpcije arsenita na adsorbent na bazi hidroksiapatita korišćenjem adaptivnog neuro-fazi sistema

This paper describes an optimization procedure for the adsorption of arsenite ions from wastewater using the Adaptive Neuro-Fuzzy Inference System (ANFIS). The adsorbent is based on hydroxy apatite, a natural material obtained from carp (Cyprinus carpio) scales. The input parameters were the influence of pH, the temperature, the initial concentration and reaction time of arsenite adsorption while the adsorption capacity and the arsenite removal percentage were studied as the output parameters. / В данной статье описана процедура оптимизации адсорбции ионов арсенита из сточных вод с использованием адаптивной нейронечеткой логической системы (ANFIS). В основе адсорбента лежит природный гидроксиапатитный материал, полученный из чешуи карпа (Cyprinus carpio). В качестве параметров ввода использовались влияние pH, температуры, начальной концентрации и времени реакции на адсорбцию арсенита, а в качестве выходных параметров были исследованы адсорбционная емкость и процент удаления арсенита. / U radu se opisuje postupak optimizacije adsorpcije arsenitnih jona iz otpadnih voda korišćenjem adaptivnog neuro-fazi sistema (ANFIS). U osnovi adsorbenta nalazi se prirodni hidroksi-apatitni materijal dobijen iz krljušti šarana (Cyprinus carpio). Kao ulazni parametri korišćeni su uticaj pH, temperature, početne koncentracije i vremena adsorpcije arsenita, a kao izlazni parametri ispitivani su adsorpcioni kapacitet i procenat uklanjanja arsenita

EQUILIBRIUM, KINETIC AND THERMODYNAMIC STUDIES ON REMOVAL OF Cd(II), Pb(II) AND As(V) FROM WASTEWATER USING CARP (CYPRINUS CARPIO) SCALES

Unmodified farming carp (cyprinus carpio) scales were used as a biosorbent for the removal of Cd(II), Pb(II) and As(V) ions from aqueous solutions. The adsorption studies were conducted as a function of pH, contact time and temperature. The best sorption of cadmium, lead and arsenate ions was achieved for pH between 6.0 and 8.0. The adsorption data for cadmium, lead and arsenate at 20, 30 and 40 °C are fitted to Langmuir, Freundlich, Sips, Dubinin-Radushkevich (D-R), Jovanovic, Jovanovic-Freundlich (J-F), Temkin, Toth and Koble-Corrigan (K-C) isotherm models. Experimental data were used to model adsorption kinetics using pseudo-first order, pseudo-second order, Elovich and intraparticle diffusion kinetic models. The results showed that the adsorption of Cd(II), Pb(II) and As(V) ions onto carp scale followed the pseudo-second-order kinetic model. Thermodynamic parameters, including the Gibbs free energy (Δ

Pulverized river shellfish shells as a cheap adsorbent for removing of malathion from water: Examination of the isotherms, kinetics, thermodynamics and optimization of the experimental conditions by the response surface method

Introduction/purpose: In this study, we investigated the possibility of removing the organophosphorus pesticide malathion from water using a new adsorbent based on the biowaste of river shell shards from the Anodonta Sinadonta woodiane family, a material that accumulates in large quantities as waste on the banks of large rivers. Two adsorbents were tested - mechanically comminuted river shells (MRM) and mechanosynthetic hydroxyapatite from comminuted river shells (RMHAp). Methods: The obtained adsorbents were characterized and tested for the removal of the organophosphorus pesticide malathion from water. In order to predict the optimal adsorption conditions using the Response Surface Method (RSM), the authors investigated the influence of variable factors (adsorption conditions), pH values, adsorbent doses, contact times, and temperatures on the adsorbent capacity. Results: The best adsorption of malathion was achieved at mean pH values between 6.0 and 7.0. The adsorption data for malathion at 25, 35, and 45 °C were compared using the Langmuir, Freundlich, DubininRadushkevich (DR), and Temkin isothermal models, as well as pseudofirst order, pseudo-second order and Elovic kinetic models for modeling adsorption kinetics. The maximum Langmuir adsorption capacity for MRM and RMHAp at 25 °C was 46,462 mg g-1 and 78,311 mg g-1 , respectively. Conclusion: The results have showed that malathion adsorption on both adsorbents follows the pseudo-second kinetic model and the Freundlich isothermal model. The thermodynamic parameters indicate the endothermic, feasible, and spontaneous nature of the adsorption process