Principles and performance analysis of SeCond: A system for epidemic peer-to-peer content distribution

We propose and design a peer-to-peer system, SeCond, addressing the distribution of large sized content to a large number of end systems in an efficient manner. In contrast to prior work, it employs a self-organizing epidemic dissemination scheme for state propagation of available blocks and initiation of block transmissions. In order to exploit heterogeneity of peers, enhance the utilization of system resources and for the ease of deployment, scalability, and adaptivity to dynamic peer arrivals/departures, we propose mechanisms for adjusting protocol parameters dynamically according to the bandwidth usages. We describe design and analysis details of our protocol SeCond. Comprehensive performance evaluations and comparison with the BitTorrent system model have been accomplished for a wide range of scenarios. Performance results include scalability analysis for different arrival/departure patterns, flash-crowd scenario, overhead analysis, and fairness ratio. The major metrics we study include the average file download time, load on the primary seed, uplink/downlink utilization, and communication overhead. We show that SeCond is a scalable and adaptive protocol which takes the heterogeneity of the peers into account. The protocol is as fair as BitTorrent although it has no explicit strategy addressing free-riding. We also illustrate the applicability of an analytical fluid model to the behavior of SeCond.     

Classification of brain hemorrhage computed tomography images using OzNet hybrid algorithm

Classification of brain hemorrhage computed tomography (CT) images provides a better diagnostic implementation for emergency patients. Attentively, each brain CT image must be examined by doctors. This situation is time-consuming, exhausting, and sometimes leads to making errors. Hence, we aim to find the best algorithm owing to a requirement for automatic classification of CT images to detect brain hemorrhage. In this study, we developed OzNet hybrid algorithm, which is a novel convolution neural networks (CNN) algorithm. Although OzNet achieves high classification performance, we combine it with Neighborhood Component Analysis (NCA) and many classifiers: Artificial neural networks (ANN), Adaboost, Bagging, Decision Tree, K-Nearest Neighbor (K-NN), Linear Discriminant Analysis (LDA), Naïve Bayes and Support Vector Machines (SVM). In addition, Oznet is utilized for feature extraction, where 4096 features are extracted from the fully connected layer. These features are reduced to have significant and informative features with minimum loss by NCA. Eventually, we use these classifiers to classify these significant features. Finally, experimental results display that OzNet-NCA-ANN excellent classifier model and achieves 100% accuracy with created Dataset 2 from Brain Hemorrhage CT images.     

Characterizations of Iron Particles Reduced from Iron Oxide Nanoparticles Under Hydrogen Atmosphere

Submicron iron particles were obtained by the reduction of co-precipitated superparamagnetic iron oxide nanoparticles under hydrogen atmosphere. The reduction was carried out at the temperatures ranging from 200 to 1000 °C. The magnetic properties were investigated in accordance with the structural properties. According to the X-ray diffraction patterns, the increase of crystallization was followed by the conversion from iron oxide to iron and also the particle size increased as the reduction temperature increased. Morphology observed by transmission electron microscope showed that the particles were individually seen at low temperatures; however, they stacked together and became larger at high temperatures. Magnetic measurements with a vibrating sample magnetometer disclosed that the saturation magnetization steadily increased with increasing temperature and almost stabilized at 800 °C. Highest saturation magnetization obtained by the reduction process is ～211 emu/g, which is close to that of bulk iron. It is disclosed that, at all temperatures, saturation magnetizations obtained from magnetic measurements were found to be compatible with the structural changes caused by reduction temperature.     

Energetic and exergetic performance analysis of Bethe-Zeldovich-Thompson (BZT) fluids in geothermal heat pumps

This study highlights theoretical energetic and exergetic performance evaluation results of Bethe-Zeldovich-Thompson (BZT) fluids in geothermal heat pumps. In this study, hexafluoroethane (R116), octafluoropropane (R218), and octafluorocyclobutane (RC318) were selected as possible alternative replacements to the traditional refrigerants in geothermal heat pumps. According to the calculations, coefficient of performance of 101 kW geothermal heat pumps (COP_HP) changes with range between 3.84, 3.08, and 2.92 for octafluorocyclobutane (RC318), hexafluoroethane (R116), and octafluoropropane (R218), respectively. Moreover, R116 shows the maximum exergy efficiency 68% among the selected BZT fluids.     

The effect of Fe content in electrodeposited CoFe/Cu multilayers on structural, magnetic and magnetoresistance characterizations

A series of CoFe/Cu multilayers were electrodeposited on Ti substrates from the electrolytes containing their metal ion under potentiostatic control, but the Fe concentration in the electrolytes was changed from 0.0125 M to 0.2 M. The deposition was carried out in a three-electrode cell at room temperature. The deposition of Cu layers was made at a cathode potential of -0.3 V with respect to saturated calomel electrode (SCE), while the ferromagnetic CoFe layers were deposited at -1.5 V versus SCE. The structural studies by X-ray diffraction revealed that the multilayers have face-centered-cubic structure. The magnetic characteristics of the films were investigated using a vibrating sample magnetometer and their easy-axis was found to be in film plane. Magnetoresistance measurements were carried out using the Van der Pauw method at room temperature with magnetic fields up to +/- 12 kOe. All multilayers exhibited giant magnetoresistance (GMR) and the GMR values up to 8% were obtained.     

Systematic investigation of optimal aptamer immobilization for protein-microarray applications

Aptamers are short single-stranded DNA or RNA oligonucleotides that can bind to a wide range of target molecules with high affinity and specificity. As nucleic acids, aptamers can undergo denaturation, but the process is reversible. As a result of this stability and the possibility of automated selection of aptamers, these oligonucleotides are highly promising capture molecules in microarray formats. In this study, his-tagged proteins and an aptamer directed against the his-tag were chosen as a model system. Different factors affect the activity of aptamers immobilized on a solid support like a microarray surface. The orientation of the immobilized aptamer plays an important role in correct aptamer folding and, thus, in effective binding of the corresponding target. Other important parameters identified in this work are the microarrays' surface charge as well as the length of the spacer between aptamer and solid support. These parameters were investigated systematically, resulting in the development of an aptamer-based microarray for detection of his-tagged proteins. The general applicability of the developed immobilization strategy was demonstrated by utilization of three different aptamers.     

Investigation of carbon contamination in lost foam castings of low carbon steel

Lost foam casting(LFC) process is a special casting method in which polymeric foam patterns with refractory coatings are utilized as a mould component. In this work, four types of foam: expandable polyethylene(EPE), expandable polypropylene(EPP) and expandable polystyrene(EPS) foams with two different densities were employed as pattern materials. LFC and conventional green sand mould casting methods were used to cast a low carbon steel, A216 Grade WCB. Both casting processes were carried out at 1,580 °C. Chemical analysis results showed that the carbon contamination level was high and was influenced by pattern type. Metallographic investigations revealed a significant increase in the percentage of pearlite phase in all LFC samples. Densities of manufactured samples were calculated in order to evaluate porosity of the products. It was determined that the densities of the LFC samples were lower than the green sand mould cast reference sample(RS). Vickers hardness tests were also carried out and increments in hardness values with increased carbon content was observed.     

Effect of Synthesis Parameters on the Properties of Superparamagnetic Iron Oxide Nanoparticles

Iron oxide nanoparticles were coprecipitated in air medium using different sodium hydroxide (NaOH) concentrations, and their structural and magnetic properties were studied. It was observed that the precipitation of superparamagnetic iron oxide nanoparticles could be achieved above a critical NaOH concentration. This was followed by the investigation of the effect of the stirring rate on the structural and magnetic properties of the nanoparticles precipitated at 8.5?M NaOH and over. Morphological observation made by a transmission electron microscope (TEM) showed that the particle size of iron oxide nanoparticles was around 7.5?nm. Magnetization curves measured by a vibrating sample magnetometer showed zero coercivity indicating that the samples are superparamagnetic and the highest saturation magnetization (70.4?emu/g) was obtained at the stirring rate of 1100?rpm. The mean particle sizes of iron oxide nanoparticles calculated from the magnetization data are found to be consistent with the particle sizes obtained from the TEM images.