Silicon phthalocyanine/methyl methacrylate copolymer slab directional couplers for all-optical switching

Slab waveguide directional couplers were fabricated from the third-order nonlinear optical polymer silicon phthalocyanine/methyl methacrylate 2/98 copolymer as the active material and poly(methyl methacrylate) as the cladding layer. The monomethacrylate copolymer was synthesized, device fabrication conditions determined, and five-layer slab directional couplers fabricated. Completed couplers demonstrated waveguide loss on the order of 5 dB/cm and coupling lengths of 0.5 mm at 1.29 μm. These vertically coupled devices may be useful for all-optical switching. © 1994 John Wiley & Sons, Inc.     

Dielectric and Thermal Investigations on PbNb2O6 in Pure Piezoelectric Phase and Pure Non-Piezoelectric Phase

A high piezoelectric transition temperature is indicated for present orthorhombic samples of lead meta-niobate (PbNb₂O₆) by the high value of the temperature (T_m) at which the real part (?′) of electrical permittivity peaks. It is 573 to 580°C. The imaginary component (?″) increases sharply at these temperatures. Cooling run after heating to 700°C gives changed values of ?′ and ?″ and T_m. Present work reports impedance spectroscopy (up to 700°C over 20 Hz to 5.5 MHz) and Differential Scanning Calorimetry or DSC on the pure orthorhombic phase, and, for the first time, on the pure rhombohedral phase. DSC shows an endothermic dip across T_m during heating and an exothermic peak during cooling for the orthorhombic sample only.     

Network flow analysis for detection and mitigation of Fraudulent Resource Consumption (FRC) attacks in multimedia cloud computing

Multimedia Tools and Applications - Multimedia computing has evolved as a remarkable technology which provides services to view, create, edit, process, and search multimedia contents. All these...     

A novel approach to defend multimedia flash crowd in cloud environment

Multimedia Tools and Applications - Cloud computing is an intelligent integration of distributed computing, hardware virtualization techniques, automated data center techniques and Internet...     

Purification of Free DHA by Selective Esterification of Fatty Acids from Tuna Oil Catalyzed by Rhizopus oryzae Lipase

Tuna fish oil contains 25–30 % docosahexaenoic acid (DHA) and is one of the richest sources of DHA. The present paper investigates the enrichment of DHA by selective esterification of fatty acids obtained from hydrolysis of tuna fish oil catalyzed by Rhizopus oryzae lipase (ROL). The fatty acid mixture obtained after hydrolysis of tuna fish oil, referred to as tuna-FFA contained 26 % DHA. For purification/concentration of DHA in free fatty acids, selective esterification of the fatty acid mixtures with butanol was carried out using ROL in a water-organic solvent system. The best reaction parameters found in this study were pH 7, temperature 35 °C, agitation speed 800 rpm and a fatty acid to solvent (iso-octane) ratio of 1:1.32 (w/v). Also, the effects of other parameters such as type of alcohol, type of enzyme, alcohol to fatty acid ratio, enzyme to fatty acid ratio were studied to determine the most suitable reaction conditions. Exactly 76.2 % of tuna-FFA was esterified in 24 h, under the most suitable reaction conditions and the DHA content in the fatty acid fraction rose from 26 to 86.9 % with 80 % recovery of DHA, after selective esterification. The DHA content of fatty acids in butyl esters was found to be 13.6 %.     

The Neonatal Fc Receptor Is Elevated in Monocyte-Derived Immune Cells in Pancreatic Cancer

The neonatal Fc receptor (FcRn) is responsible for recycling of IgG antibodies and albumin throughout the body. This mechanism has been exploited for pharmaceutic delivery across an array of diseases to either enhance or diminish this function. Monoclonal antibodies and albumin-bound nanoparticles are examples of FcRn-dependent anti-cancer therapeutics. Despite its importance in drug delivery, little is known about FcRn expression in circulating immune cells. Through time-of-flight mass cytometry (CyTOF) we were able to characterize FcRn expression in peripheral blood mononuclear cell (PBMC) populations of pancreatic ductal adenocarcinoma (PDAC) patients and non-cancer donors. Furthermore, we were able to replicate these findings in an orthotopic murine model of PDAC. Altogether, we found that in both patients and mice with PDAC, FcRn was elevated in migratory and resident classical dendritic cell type 2 (cDC2) as well as monocytic and granulocytic myeloid-derived suppressor cell (MDSC) populations compared to tumor-free controls. Furthermore, PBMCs from PDAC patients had elevated monocyte, dendritic cells and MDSCs relative to non-cancer donor PBMCs. Future investigations into FcRn activity may further elucidate possible mechanisms of poor efficacy of antibody immunotherapies in patients with PDAC.     

Deep feature extraction and classification of breast ultrasound images

Controlled despeckling (structure/edges/feature preservation with smoothing the homogeneous areas) is a desired pre-processing step for the design of computer-aided diagnostic (CAD) systems using ultrasound images as the presence of speckle noise masks diagnostically important information making interpretation difficult even for experienced radiologist. For efficiently classifying the breast tumors, the conventional CAD system designs use hand-crafted features. However, these features are not robust to the variations in size, shape and orientation of the tumors resulting in lower sensitivity. Thus deep feature extraction and classification of breast ultrasound images have recently gained attention from research community. The deep networks come with an advantage of directly learning the representative features from the images. However, these networks are difficult to train from scratch if the representative training data is small in size. Therefore transfer learning approach for deep feature extraction and classification of medical images has been widely used. In the present work the performance of four pre-trained convolutional neural networks VGG-19, SqueezeNet, ResNet-18 and GoogLeNet has been evaluated for differentiating between benign and malignant tumor types. From the results of the experiments, it is noted that CAD system design using GoogLeNet architecture for deep feature extraction followed by correlation based feature selection and fuzzy feature selection using ANFC-LH yields highest accuracy of 98.0% with individual class accuracy value of 100% and 96% for benign and malignant classes respectively. For differentiating between the breast tumors, the proposed CAD system design can be utilized in routine clinical environment.