Solubility Enhancement of Fe in ZnO Nanoparticles Prepared by Co-Precipitation Method

Journal of Superconductivity and Novel Magnetism - Crystalline ZnO offers an excellent host matrix to create a dilute magnetic semiconductor (DMS) owing to its facile Zn-atom substitution with the...     

An Investigation of the Effect of Sintering Conditions on the Mechanical Behavior of Electroplated Nickel Foams

Metallurgical and Materials Transactions B - This study investigates the effect of sintering temperature on the compression strength of nickel foams in an inert atmosphere. The nickel foams were...     

Intensity-based statistical features for classification of lungs CT scan nodules using artificial intelligence techniques

A computer-aided diagnostic (CAD) system for effective and accurate pulmonary nodule detection is required to detect the nodules at early stage. This paper proposed a novel technique to detect and classify pulmonary nodules based on statistical features for intensity values using support vector machine (SVM). The significance of the proposed technique is, it uses the nodules features in 2D & 3D and also SVM for the classification that is good to classify the nodules extracted from the image. The lung volume is extracted from Lung CT using thresholding, background removal, hole-filling and contour correction of lung lobe. The candidate nodules are extracted and pruned using the rules based on ground truth of nodules. The statistical features for intensity values are extracted from candidate nodules. The nodule data are up-samples to reduce the biasness. The classifier SVM is trained using data samples. The efficiency of proposed CAD system is tested and evaluated using Lung Image Consortium Database (LIDC) that is standard data-set used in CAD Systems for Lungs Nodule classification. The results obtained from proposed CAD system are good as compare to previous CAD systems. The sensitivity of 96.31% is achieved in the proposed CAD system.     

An Optimal Solution for Test Case Generation Using ROBDD Graph and PSO Algorithm

Software testing is one of the most important techniques to examine the behavior of the software products to assure their quality. An effective and efficient testing approach must balance two important but conflicting requirements. One of them is the accuracy that needs a large number of test cases for testing, and the second one is reducing the time and cost, which requires a few test cases. Even for small software, the number of possible test cases is typically very large, and exhaustive testing is impractical. Hence, selecting appropriate test suite is necessary. Cause–effect graph testing is a common black‐box testing technique, which is equivalently representing Boolean relations between input parameters. However, the other traditional black‐box strategies cannot identify the relations that it may result in loss of some of the important test cases. Although the cause–effect graph is regarded very promising in specification testing, it is observed that most of the proposed approaches using the graph are complex or generate impossible and a large number of test cases. This observation has motivated our research to propose an efficient strategy to generate minimal test suite that simultaneously achieves high coverage of input parameters. To do so, at first, we identify major effects from the cause–effect graph using reduced ordered binary decision diagram (ROBDD). ROBDD makes the related Boolean expression of the graph concise and obtains a unique representation of the expression. Using the ROBDD, it is possible to reduce the size of the generated test suite and to perform testing faster. After that, our proposed method utilizes particle swarm optimization (PSO) algorithm to select the optimal test suite, which covers all pairwise combinations of input parameters. The experimental results show that our approach simultaneously achieves high efficacy and reduces cost of testing by selecting appropriate test cases, respectively, to both test size and coverage size. Also, it outperforms some existing state‐of‐the‐art strategies in the black‐box testing. Copyright © 2015 John Wiley & Sons, Ltd.     

Ultrathin buffer layer at organic/organic interface for managing the recombination profile in organic light‐emitting diodes: Metal versus dielectric buffer

We report on the utilization of an ultrathin buffer layer at the organic/organic (O/O) interface to enhance device efficiency in organic light‐emitting diodes. Two different kinds of buffer layers are examined: metal and dielectric. It is shown that employment of an ultrathin Ag layer with a thickness of 1–2 nm enhances the device performance, while a MgF₂ dielectric buffer cannot affect the device properties considerably. In particular, the turn‐on voltage of the device with an appropriate buffer layer is reduced about 3 V, its current efficiency increases by a factor of more than three, and the power efficiency increases by a factor of more than five in comparison to the control device when a Ag buffer layer is introduced at the O/O interface. By employment of the buffer layer at the interface, an accumulation of current carriers appears within the device that redistribute the recombination profile toward the interior part of the emissive layer. Also, morphological examinations reveal that distinguishable phase segregation occurs in the blend of the hole‐transport layer. In particular, the polymer component remains at the surface and facilitates the hole transport into the successive layers. © 2016 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2016 , 133, 43894.     

Long Duration Persistent Photocurrent in 3 nm Thin Doped Indium Oxide for Integrated Light Sensing and In-Sensor Neuromorphic Computation

Miniaturization and energy consumption by computational systems remain major challenges to address. Optoelectronics based synaptic and light sensing provide an exciting platform for neuromorphic processing and vision applications offering several advantages. It is highly desirable to achieve single-element image sensors that allow reception of information and execution of in-memory computing processes while maintaining memory for much longer durations without the need for frequent electrical or optical rehearsals. In this work, ultra-thin (<3 nm) doped indium oxide (In₂O₃) layers are engineered to demonstrate a monolithic two-terminal ultraviolet (UV) sensing and processing system with long optical state retention operating at 50 mV. This endows features of several conductance states within the persistent photocurrent window that are harnessed to show learning capabilities and significantly reduce the number of rehearsals. The atomically thin sheets are implemented as a focal plane array (FPA) for UV spectrum based proof-of-concept vision system capable of pattern recognition and memorization required for imaging and detection applications. This integrated light sensing and memory system is deployed to illustrate capabilities for real-time, in-sensor memorization, and recognition tasks. This study provides an important template to engineer miniaturized and low operating voltage neuromorphic platforms across the light spectrum based on application demand.     

An Insight View on Synthetic Protocol,Surface Activity,and Biological Aspects of Novel Biocompatible Quaternary Ammonium Cationic Gemini Surfactants

In this study, we prepared a novel series of diester-functionalized cationic gemini surfactants (C_m-E2O2-C_m) containing ethylene oxide as a spacer with varying alkyl chain lengths and characterized by ¹H NMR, FT-IR, elemental analysis, and ESI-MS. The physicochemical properties of the geminis were explored by tensiometry, fluorescence, dye solubilization, and Krafft point. These geminis acquire superior surface activity than the conventional surfactants. Fluorescence spectroscopy analysis affirmed that the micropolarity and aggregation number of micelles diminished with increase in the alkyl chain length. These geminis represent a new group of amphiphiles of considerably high biodegradability, better cleavability, and low toxicity as assessed by BOD test, FT-IR analysis, and HC₅₀ analysis, respectively. They also showed significant level of antimicrobial activity toward some specified bacterial strains of Gram-positive and Gram-negative by using agar well diffusion method. Furthermore, the thermogravimetric analysis provided information regarding thermal stabilities of the newly synthesized gemini surfactants.     

Nano SiC-Nickel Composite Coatings from a Sulfamat Bath Using Direct Current and Pulsed Direct Current

Composite plating is a method through which the fine particles of metallic or non-metallic compounds are co-deposited in a plated layer to improve surface properties such as lubrication, wear resistance, and corrosion resistance. In this study, nano-sized SiC particles were co-deposited with nickel from sulfamat bath using pulsed and direct currents. Scanning electron microscopy, microhardness, wear, and corrosion tests were carried out to characterize the coating properties. The results showed that microhardness, wear resistance, and corrosion resistance of Ni-SiC composite coatings increased compared to those of Ni films.