Characterization of unextractable organic matter associated with heavy minerals from oil sand

Considerable quantities of organic matter are associated with heavy metal minerals recovered from Suncor aqueous sludge. This organic matter is not extractable with common organic solvents. Attempts were made to concentrate this organic matter by dissolving the mineral matter in acids. Various soluble fractions were also obtained by extraction with methanol/benzene (1:4) after each dissolution step. All these fractions were analysed by elemental analysis, infrared, proton and ¹³C nuclear magnetic resonance spectroscopy.     

Novel designs of nanometric parity preserving reversible compressor

Reversible logic is a new field of study that has applications in optical information processing, low power CMOS design, DNA computing, bioinformatics, and nanotechnology. Low power consumption is a basic issue in VLSI circuits today. To prevent the distribution of errors in the quantum circuit, the reversible logic gates must be converted into fault-tolerant quantum operations. Parity preserving is used to realize fault tolerant in this circuits. This paper proposes a new parity preserving reversible gate. We named it NPPG gate. The most significant aspect of the NPPG gate is that it can be used to produce parity preserving reversible full adder circuit. The proposed parity preserving reversible full adder using NPPG gate is more efficient than the existing designs in term of quantum cost and it is optimized in terms of number of constant inputs and garbage outputs. Compressors are of importance in VLSI and digital signal processing applications. Effective VLSI compressors reduce the impact of carry propagation of arithmetic operations. They are built from the full adder blocks. We also proposed three new approaches of parity preservation reversible 4:2 compressor circuits. The third design is better than the previous two in terms of evaluation parameters. The important contributions have been made in the literature toward the design of reversible 4:2 compressor circuits; however, there are not efforts toward the design of parity preservation reversible 4:2 compressor circuits. All the scales are in the nanometric criteria.     

Synthesizing and stabilizing silver nanoparticles on polyamide fabric using silver-ammonia/PVP/UVC

A colloidal dispersion of silver nanoparticles were prepared with Tollens’ reagent [Ag(NH₃)₂]⁺ and polyvinyl pyrrolidone (PVP) as a reducing/stabilizing agent trough UVC irradiation and then applied on the nylon fabric by using a simple pad method. The ultraviolet irradiation was assisted to reduce Ag⁺ to Ag⁰. The presence of nanosilver in the solution and on the fabric was confirmed by UV–vis, EDX, SEM and XRD. In addition, the role of PVP as a stabilizing agent on the nylon surface was investigated. Further, an antibacterial test was carried out on the fabrics in the presence of two bacteria namely Staphylococcus aureus and Escherichia coli. The treated fabric with 200 ppm of the above mentioned solution was reduced the bacteria up to 99.2% after 20 washes. Some other properties of the fabric such as color variations, dimensional stability, water droplet adsorption and reflectance spectrum were also carried out and the results thoroughly discussed.     

Deploying swarm intelligence in medical imaging identifying metastasis,micro‐calcifications and brain image segmentation

This study proposes an umbrella deployment of swarm intelligence algorithm, such as stochastic diffusion search for medical imaging applications. After summarising the results of some previous works which shows how the algorithm assists in the identification of metastasis in bone scans and microcalcifications on mammographs, for the first time, the use of the algorithm in assessing the CT images of the aorta is demonstrated along with its performance in detecting the nasogastric tube in chest X‐ray. The swarm intelligence algorithm presented in this study is adapted to address these particular tasks and its functionality is investigated by running the swarms on sample CT images and X‐rays whose status have been determined by senior radiologists. In addition, a hybrid swarm intelligence‐learning vector quantisation (LVQ) approach is proposed in the context of magnetic resonance (MR) brain image segmentation. The particle swarm optimisation is used to train the LVQ which eliminates the iteration‐dependent nature of LVQ. The proposed methodology is used to detect the tumour regions in the abnormal MR brain images.Inspec keywords: swarm intelligence, image segmentation, brain, neurophysiology, medical image processing, biomedical MRI, computerised tomography, diagnostic radiography, bone, diseases, learning (artificial intelligence), particle swarm optimisation, iterative methods, tumours, medical disordersOther keywords: medical imaging identifying metastasis, microcalcifications, umbrella deployment, stochastic diffusion, metastasis identification, bone scans, mammographs, CT imaging, aorta, nasogastric tube, chest X‐ray, hybrid swarm intelligence‐learning vector quantisation approach, magnetic resonance brain image segmentation, particle swarm optimisation, iteration‐dependent nature, tumour regions, abnormal MR brain imaging     

Efficient Timing Budget Management for Accuracy Improvement in a Collaborative Object Tracking System

This paper presents the idea of managing the comprising computations of an application performed by an embedded networked system. An efficient algorithm for exploiting the timing slack of building blocks of the application is proposed. The slack of blocks can be utilized by replacing them with slower but cheaper, i.e. better, modules and by assigning the computations to the proper resources. Thus, our approach manages the comprising computations and system resources and can indirectly assist the realtime scheduling of computations on system resources. This is performed without compromising the timing constraints of the application and can lead to significant improvements in power dissipation, computation accuracy or other metrics of the application domain. Our algorithm is well-suited for arbitrary tree computations. Moreover, it delivers solutions that are desirably close to the optimal solution. Experimental results for a number of object tracking applications implemented in an networked system with embedded computation resources, exhibit a significant amount of slack utilization. Soheil Ghiasi received his B.S. from Sharif University of Technology, Tehran, Iran in 1998, and his M.S. and Ph.D. in Computer Science from the University of California, Los Angeles in 2002 and 2004, respectively. Currently, he is an assistant professor in the department of electrical and computer engineering at the University of California, Davis. His research interests include different aspects of Embedded and Reconfigurable system design. Elaheh Bozorgzadeh received the B.S. degree in Electrical Engineering from Sharif University of Technology, Iran in 1998, M.S. degree in Computer Engineering from Northwestern University in 2000, and Ph.D. degree in Computer Science from the University of California, Los Angeles, in 2003. She is currently as assistant professor in the Department of Computer Science at the University of California, Irvine. Her research interest includes VLSI CAD, design automation for embedded systems, and reconfigurable computing. She is a member of ACM and IEEE. Karlene Nguyen received her B.S. and M.S. from University of California, Los Angeles in 2001 and 2003, respectively. She has been working with Prof. Majid Sarrafzadeh for her M.S. degree. Her research interests include embedded hardware and software design. Majid Sarrafzadeh received his B.S., M.S. and Ph.D. in 1982, 1984, and 1987 respectively from the University of Illinois at Urbana-Champaign in Electrical and Computer Engineering. He joined Northwestern University as an Assistant Professor in 1987. In 2000, he joined the Computer Science Department at University of California at Los Angeles (UCLA). His recent research interests lie in the area of Embedded and Reconfigurable Computing, VLSI CAD, and design and analysis of algorithms. Dr. Sarrafzadeh is a Fellow of IEEE for his contribution to “Theory and Practice of VLSI Design.” He received an NSF Engineering Initiation award, two distinguished paper awards in ICCAD, and the best paper award in DAC. He has served on the technical program committee of numerous conferences in the area of VLSI Design and CAD, including ICCAD, DAC, EDAC, ISPD, FPGA, and DesignCon. He has served as committee chairs of a number of these conferences. He is on the executive committee/steering committee of several conferences such as ICCAD, ISPD, and ISQED. He is the program committee chair of ICCAD 2004. Professor Sarrafzadeh has published approximately 250 papers, is a co-editor of the book “Algorithmic Aspects of VLSI Layout” (1994 by World Scientific), and co-author of the book “An Introduction to VLSI Physical Design” (1996 by McGraw Hill). Dr. Sarrafzadeh is an Associate Editor of ACM Transaction on Design Automation (TODAES) and an Associate Editor of IEEE Transactions on Computer-Aided Design (TCAD) and ACM Transactions on design Automation (TODAES). Dr. Sarrafzadeh has collaborated with many industries in the past fifteen years including IBM, Motorola, and many CAD industries. He is the architect of the physical design subsystem of Monterey Design Systems main product. He is a co-founder of Hier Design, Inc.     

Synthesis and characterization of conductive neural tissue engineering scaffolds based on urethane-polycaprolactone

Polymeric biomaterials play a key role in enhancement of lengthy nerve regeneration and various types of scaffolds were used to pave the way for nerve regeneration. Electrospun fibrous scaffolds have special potential applicability in controlling the cell behaviors such as adhesion, growth, proliferation and function. This study attempted to design a conductive and porous fibrous scaffold containing polycaprolactone (PCL) and polyaniline (PANI) with controllable degradation rate by adding urethane groups in scaffold structures. FTIR and NMR analysis was used to characterize the chemical bonds. Morphology, porosity, conductivity and degradation rate of scaffolds were also evaluated. To assess the cell–scaffold interaction, PC-12 cell line was cultured on the scaffolds. Results showed that the degradation rate of composite samples significantly increased in 50 time period. It seems that these results suggest that the composite fibrous scaffolds having proportions of UPCL/PCL/PANI45:20:35 exhibit the most balanced properties that meet all of the required specifications for neural cells and possess a potential application in neural tissue engineering.     

Electro-conductive modification of polyethylene terephthalate fabric with nano carbon black and washing fastness improvement by dopamine self-polymerized layer

Modification of textiles with new applications target such as electroconductive fabrics has recently attracted researchers. In this article, carbon black nanoparticles (CB NPs) were applied to polyester fabric through two separate high temperature (HT) exhaustion processing with NaOH and cetyltrimethylammonium bromide (CTAB) as alkali hydrolysis catalyst and dispersing agents. To improve the stability of CB NPs on the fabric a self-polymerized compound, dopamine (DA) was used in a simple dipping method at room temperature for 24 h to form a thin layer of PDA on CB NPs-treated fabric. Field emission scanning electron microscopy (FESEM) was used to study the morphology of the fabrics confirming presence of CB NPs. EDX and mapping patterns showed the percentage and distribution of carbon, nitrogen, and oxygen elements on the fabric surface. The treated fabric indicated an electrical resistance of 14 kΩ turns a LED device on with a 10 V power supply. Self-polymerized DA on the fabric surface led to more nitrogen and oxygen caused higher CB NPs stability. Furthermore, the tensile strength results revealed a 25.8% lower tenacity on the treated fabric. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019 , 136, 48035.     

MgO-CeO2 nanocomposite: efficient catalyst for the preparation of 2-aminothiophenes and thieno[2,3-d]pyrimidin-4(3H)-one derivatives

MgO-CeO₂ nanocomposite was prepared by a co-precipitation method and characterized by X-ray diffraction (XRD), FE-SEM, and particle size distribution analysis. The XRD pattern shows the cubic phase of cerium oxide as the dominate phase. FE-SEM images show the homogeneity distribution of magnesium and cerium oxides in the sample. The mean particle size of nanocomposite determined by the dynamic light scattering technique is 66?nm. The catalytic activity of MgO-CeO₂ nanocomposite was examined on the synthesis of 2-aminothiophenes and thieno[2,3-d]pyrimidin-4(3H)-one derivatives. In all cases, products were obtained in good to excellent yields.     

A coloured polyester fabric with antimicrobial properties conferred by copper nanoparticles

In this study, we aimed to produce a coloured polyester fabric through the in situ sonosynthesis of copper nanoparticles using copper sulphate, hydrazine, sodium hydroxide and polyvinylpyrrolidone. The treated fabrics were characterised by X‐ray diffraction, field emission scanning electron microscopy, energy‐dispersive X‐ray spectroscopy and elemental mapping. Moreover, mechanical properties, wettability and antibacterial/antifungal activities of the treated fabrics were evaluated. Central composite design based on the response surface methodology was used to study the effect of copper sulphate, hydrazine hydrate and sodium hydroxide on the weight gain and colour of the treated fabrics. In addition to their roles as reducing agents, hydrazine and sodium hydroxide were responsible for the simultaneous aminolysis and hydrolysis of polyester, increasing the adsorption of nanoparticles on the surface. According to the results, the reddish brown samples treated with copper nanoparticles showed excellent antibacterial and antifungal efficiencies, improved tensile strength and decreased wettability.