Electrical diagnostics of laser ablated platinum plasma

The most significant parameters of laser-induced plasma are investigated here experimentally. Pure platinum targets of 4 N were irradiated both in air and under vacuum (∼10⁻³ Torr) by Q-switched Nd: YAG laser (1064 nm, 10 mJ, 9-14 ns, 1.1 MW). A self fabricated Langmuir probe was used as an electric diagnostic tool. Varying voltages were applied to the probe and the corresponding values of currents were recorded to draw current-voltage (I-V) characteristics curves. The electronic signals were recorded on 500 MHz DL1740, YOKOGAWA four channels digital storage oscilloscope. Different plasma characteristics like electron temperature, electron density, Debye's length, plasma frequency and numbers of particles in the “Debye's Sphere” were calculated both in air and under vacuum.     

Development of Polyoxyethylene Zwitterionic Surfactants for High-Salinity High-Temperature Reservoirs

Substantial efforts are underway to improve the recovery factor from existing oil reserves to meet the ever-growing global oil demand. Surfactants are known to increase oil recovery through reducing interfacial tension (IFT) and/or altering the rock wettability. The selection of surfactants for high-salinity high-temperature oil fields is a challenging task owing to poor thermal stability, precipitation, and adsorption of surfactants on reservoir rocks. Sulfobetaine-based polyoxyethylene zwitterionic surfactants have shown excellent thermal and surface properties. However, their solubility in high-salinity brines becomes poor particularly with a long hydrophobic tail (>C17). Recently, we synthesized such types of surfactants by incorporating ethylene oxide (EO) units into the hydrophobic tail, which improved the solubility in formation water (213,734 ppm) and seawater (SW) (57,643 ppm). In this work, we investigated the IFT, thermal stability, rheological behavior, and foaming properties of two polyoxyethylene zwitterionic surfactants having different degrees of ethoxylation. Aging experiments exhibited excellent thermal stability and no change in the chemical structure was detected. The surfactant with lesser EO units (EASB-1a) showed a lower IFT compared to the surfactant with higher EO units (EASB-1b). Rheological studies revealed that the addition of both surfactants reduced the viscosity of the acrylamide copolymer. However, the effect of EASB-1a was more prominent compared to that of EASB-1b. The surfactant with a higher degree of ethoxylation showed lower adsorption compared to the surfactant with a lesser degree of ethoxylation. Both surfactants showed excellent foamability and foam stability compared to the commercial surfactants. Excellent thermal stability, water solubility under harsh reservoir conditions, foaming properties, and lower adsorption make them a suitable choice for high-temperature, high-salinity reservoirs.     

Automated techniques for blood vessels segmentation through fundus retinal images: A review

Retina is the interior part of human's eye, has a vital role in vision. The digital image captured by fundus camera is very useful to analyze the abnormalities in retina especially in retinal blood vessels. To get information of blood vessels through fundus retinal image, a precise and accurate vessels segmentation image is required. This segmented blood vessel image is most beneficial to detect retinal diseases. Many automated techniques are widely used for retinal vessels segmentation which is a primary element of computerized diagnostic systems for retinal diseases. The automatic vessels segmentation may lead to more challenging task in the presence of lesions and abnormalities. This paper briefly describes the various publicly available retinal image databases and various machine learning techniques. State of the art exhibited that researchers have proposed several vessel segmentation methods based on supervised and supervised techniques and evaluated their results mostly on publicly datasets such as digital retinal images for vessel extraction and structured analysis of the retina. A comprehensive review of existing supervised and unsupervised vessel segmentation techniques or algorithms is presented which describes the philosophy of each algorithm. This review will be useful for readers in their future research.     

A New Hybrid SARFIMA-ANN Model for Tourism Forecasting

Many countries developed and increased greenery in their country sights to attract international tourists. This planning is now significantly contributing to their economy. The next task is to facilitate the tourists by sufficient arrangements and providing a green and clean environment; it is only possible if an upcoming number of tourists’ arrivals are accurately predicted. But accurate prediction is not easy as empirical evidence shows that the tourists’ arrival data often contains linear, nonlinear, and seasonal patterns. The traditional model, like the seasonal autoregressive fractional integrated moving average (SARFIMA), handles seasonal trends with seasonality. In contrast, the artificial neural network (ANN) model deals better with nonlinear time series. To get a better forecasting result, this study combines the merits of the SARFIMA and the ANN models and the purpose of the hybrid SARFIMA-ANN model. Then, we have used the proposed model to predict the tourists’ arrival in New Zealand, Australia, and London. Empirical results showed that the proposed hybrid model outperforms in predicting tourists’ arrival compared to the traditional SARFIMA and ANN models. Moreover, these results can be generalized to predict tourists’ arrival in any country or region with a complicated data pattern.     

Aggregation and clogging phenomena of rigid microparticles in microfluidics

We developed a numerical tool to investigate the phenomena of aggregation and clogging of rigid microparticles suspended in a Newtonian fluid transported through a straight microchannel. In a first step, we implement a time-dependent one-way coupling Discrete Element Method (DEM) technique to simulate the movement and effect of adhesion on rigid microparticles in two- and three-dimensional computational domains. The Johnson–Kendall–Roberts (JKR) theory of adhesion is applied to investigate the contact mechanics of particle–particle and particle–wall interactions. Using the one-way coupled solver, the agglomeration, aggregation and deposition behavior of the microparticles is studied by varying the Reynolds number and the particle adhesion. In a second step, we apply a two-way coupling CFD–DEM approach, which solves the equation of motion for each particle, and transfers the force field corresponding to particle–fluid interactions to the CFD toolbox OpenFOAM. Results for the one-way (DEM) and two-way (CFD–DEM) coupling techniques are compared in terms of aggregate size, aggregate percentages, spatial and temporal evaluation of aggregates in 2D and 3D. We conclude that two-way coupling is the more realistic approach, which can accurately capture the particle–fluid dynamics in microfluidic applications.     

Ultra-thin Laminated Metal Composites with Ultra-high Strength and Excellent Soft Magnetic Properties

Structural metallic materials with excellent functional performance and lightweight features have always been the goal of material scientists' pursuit.In this work,laminated metal composites of different thicknesses(less than 0.4 mm) composed of structural materials with great differences in deformation ability were successfully fabricated via a novel processing procedure.Ultra-high strength and excellent soft magnetic properties were combined perfectly in the ultra-thin and super-light laminated metal composite strips due to unique structural design and essential attributes of the initial materials.These results emphasize the significant potential application value of the ultra-thin laminated metal composites in the field of structural and functional integration.     

A correlation-based ant miner for classification rule discovery

In recent years, a few sequential covering algorithms for classification rule discovery based on the ant colony optimization meta-heuristic (ACO) have been proposed. This paper proposes a new ACO-based classification algorithm called AntMiner-C. Its main feature is a heuristic function based on the correlation among the attributes. Other highlights include the manner in which class labels are assigned to the rules prior to their discovery, a strategy for dynamically stopping the addition of terms in a rule’s antecedent part, and a strategy for pruning redundant rules from the rule set. We study the performance of our proposed approach for twelve commonly used data sets and compare it with the original AntMiner algorithm, decision tree builder C4.5, Ripper, logistic regression technique, and a SVM. Experimental results show that the accuracy rate obtained by AntMiner-C is better than that of the compared algorithms. However, the average number of rules and average terms per rule are higher.     

Image-driven virtual simulation of arthroscopy

In recent years, minimally invasive arthroscopic surgery has replaced a number of conventional open orthopedic surgery procedures on joints. While this achieves a number of advantages for the patient, the surgeons have to learn very different skills, since the surgery is performed with special miniature pencil-like tools and cameras inserted through little incisions while observing the surgical field on video monitor. Therefore, virtual reality simulation becomes an alternative to traditional surgical training based on hundreds years old apprentice–master model that involves either real patients or increasingly difficult to procure cadavers. Normally, 3D simulation of the virtual surgical field requires significant efforts from the software developers but yet remains not always photorealistic. In contrast to this, for photorealistic visualization and haptic interaction with the surgical field we propose to use real arthroscopic images augmented with 3D object models. The proposed technique allows for feeling the joint cavity displayed on video monitor as real 3D objects rather than their images while various surgical procedures, such as menisectomy, are simulated in real time. In the preprocessing stage of the proposed approach, the arthroscopic images are stitched into panoramas and augmented with implicitly defined object models representing deformable menisci. In the simulation loop, depth information from the mixed scene is used for haptic rendering. The scene depth map and visual display are reevaluated only when the scene is modified.     

A comparison of TiO2 nanoparticles and nanotubes for catalytic gas phase destruction of H2S gas at high temperatures

Reduction of H2S gas over Sulphur doped TiO2 nanoparticles and TiO2 nanotubes was studied in this work. Fixed bed catalytic system was used for the catalytic reduction of H2S gas at a high temperature of 450 degrees C under laboratory conditions. 99.97% reduction was achieved using S-doped TiO2. 2.89% Sulphur was adsorbed on S-doped TiO2 nanoparticles in the form of Ti(SO4)2, while 95.6% reduction was achieved in case of TiO2 nanotubes and the sulphur adsorption was 2.67%. The XRD, SEM, and EDX techniques were carried out to characterize the nanoparticles and nanotubes, while gas reduction analysis was carried out using GC-MS for gas samples.