Configurational and nonconfigurational interactions between odorants in binary mixtures.

Studies on odor mixture perception suggest that although odor components can often be identified in mixtures, mixtures can also give rise to novel perceptual qualities that are not present in the components. Using an olfactory habituation task, the authors evaluated how the perceptual similarity between components in a mixture affects the perceptual quality of the mixture itself. Rats perceived binary mixtures composed of similar components as different from their 2 components, whereas binary mixtures composed of dissimilar components were perceived as very similar to their components. Results show that for both types of mixtures, pretraining to Component A reduces subsequent learning about Component B in rats trained in the presence of A. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Photophysical studies of copolymers of N‐vinylcarbazole

The absorption, fluorescence excitation and emission spectroscopy, and time‐dependent spectrofluorimetry have been used to study the photophysics of copolymers of N‐vinylcarbazole with different monomers like vinyl acetate, methyl acrylate, methyl methacrylate, butyl acrylate, and butyl methacrylate in dichloromethane. In all the copolymers and at different N‐vinylcarbazole content, the absorption spectra reflect only the monomer carbazole units. The two kinds of excited monomer species of N‐vinylcarbazole are present in S₁ state. Short‐lived (～3 ns) excited monomer decays forming low energy excimer obtained by the complete overlap of the excited carbazole monomer. The long‐lived excited monomer (～8 ns) decays to ground state without formation of any excimer. The high energy excimer is relatively short‐lived and is formed by the partial overlap of the carbazole units. The presence of bulky group in the copolymer chain hinders the formation of excimers. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 100: 372–380, 2006     

Exercise classification and event segmentation in Hammersmith Infant Neurological Examination videos

Image and video processing techniques are being frequently used in medical science applications. Computer vision-based systems have successfully replaced various manual medical processes such as analyzing physical and biomechanical parameters, physical examination of patients. These systems are gaining popularity because of their robustness and the objectivity they bring to various medical procedures. Hammersmith Infant Neurological Examinations (HINE) is a set of physical tests that are carried out on infants in the age group of 3–24 months with neurological disorders. However, these tests are graded through visual observations, which can be highly subjective. Therefore, computer vision-aided approach can be used to assist the experts in the grading process. In this paper, we present a method of automatic exercise classification through visual analysis of the HINE videos recorded at hospitals. We have used scale-invariant-feature-transform features to generate a bag-of-words from the image frames of the video sequences. Frequency of these visual words is then used to classify the video sequences using HMM. We also present a method of event segmentation in long videos containing more than two exercises. Event segmentation coupled with a classifier can help in automatic indexing of long and continuous video sequences of the HINE set. Our proposed framework is a step forward in the process of automation of HINE tests through computer vision-based methods. We conducted tests on a dataset comprising of 70 HINE video sequences. It has been found that the proposed method can successfully classify exercises with accuracy as high as 84%. The proposed work has direct applications in automatic or semiautomatic analysis of “vertical suspension” and “ventral suspension” tests of HINE. Though some of the critical tests such as “pulled-to-sit,” “lateral tilting,” or “adductor’s angle measurement” have already been addressed using image- and video-guided techniques, scopes are there for further improvement.     

Influence of Al doping in LaCoO3 on structural,electrical and magnetic properties

We report investigations on polycrystalline LaCo_1?x Al _x O₃ (x = 0–0.9) bulk samples. The solid state synthesized samples showed a coexistence of rhombohedral and monoclinic phases in the intermediate concentrations (0.2 ≤ x ≤ 0.5) and pure rhombohedral phase otherwise. The observed effect of Al doping on dc transport has been analysed on the basis of small polaron hopping mechanism. The magnetisation results presented give evidence of weak ferromagnetism and anomalous temperature dependence of coercivity which we associate to the canting of the localised high-spin Co(III) and anti-symmetric exchange interactions at low temperatures.     

Performance evaluation of CBN,coated carbide,cryogenically treated uncoated/coated carbide inserts in finish-turning of hardened steel

In the present work, the performance of cubic boron nitride (CBN) inserts was compared with coated carbide and cryogenically treated coated/uncoated carbide inserts in terms of flank wear, surface roughness, white layer formation, and microhardness variation under dry cutting conditions for finish turning of hardened AISI H11 steel (48–49 HRC). The flank wear of CBN tools was observed to be lower than that of other inserts, but the accumulated machining time for all the four edges of carbide inserts were nearer to or better than the PCBN inserts. Results showed that tool life of carbide inserts decreased at higher cutting speeds. The surface roughness achieved under all cutting conditions for coated-carbide-treated/untreated inserts was comparable with that achieved with CBN inserts and was below 1.6 μm. The white layer formation and microhardness variation is less while turning with cryogenically treated carbide inserts than the CBN and untreated carbide. At low to medium cutting speed and feed, the performance of carbide inserts was comparable with CBN both in terms of tool life and surface integrity.     

Synthesis of semi interpenetrating network hydrogel [(GrA‐Psy)‐cl‐Poly (AA)] and its application for efficient removal of malachite green from aqueous solution

In the present study, the synthesis of novel semi‐interpenetrating network (Semi‐IPN) hydrogel based on gum rosin alcohol‐psyllium (GrA‐Psy) has been reported. (Gum rosin alcohol‐psyllium)‐crosslinked‐poly(acrylic acid) [termed as (GrA‐Psy)‐cl‐poly (AA)] was synthesized using potassium persulphate (KPS) as an initiator and N, N^′? methylene‐bis‐acrylamide (MBA) as crosslinker by free radical graft co‐polymerization technique. The prepared semi‐IPN was characterized using FTIR, XRD, and FE‐SEM. Further, the synthesized hydrogel was evaluated for the removal of malachite green (MG) dye from aqueous solution. Dye adsorption data was analyzed through various isotherm and kinetic models. Thermodynamic parameters and reusability of the synthesized adsorbent were also assessed. POLYM. ENG. SCI., 59:1416–1427 2019. © 2019 Society of Plastics Engineers     

MRMR Based Feature Vector Design for Efficient Citrus Disease Detection

In recent times, the images and videos have emerged as one of the most important information source depicting the real time scenarios. Digital images nowadays serve as input for many applications and replacing the manual methods due to their capabilities of 3D scene representation in 2D plane. The capabilities of digital images along with utilization of machine learning methodologies are showing promising accuracies in many applications of prediction and pattern recognition. One of the application fields pertains to detection of diseases occurring in the plants, which are destroying the widespread fields. Traditionally the disease detection process was done by a domain expert using manual examination and laboratory tests. This is a tedious and time consuming process and does not suffice the accuracy levels. This creates a room for the research in developing automation based methods where the images captured through sensors and cameras will be used for detection of disease and control its spreading. The digital images captured from the field's forms the dataset which trains the machine learning models to predict the nature of the disease. The accuracy of these models is greatly affected by the amount of noise and ailments present in the input images, appropriate segmentation methodology, feature vector development and the choice of machine learning algorithm. To ensure the high rated performance of the designed system the research is moving in a direction to fine tune each and every stage separately considering their dependencies on subsequent stages. Therefore the most optimum solution can be obtained by considering the image processing methodologies for improving the quality of image and then applying statistical methods for feature extraction and selection. The training vector thus developed is capable of presenting the relationship between the feature values and the target class. In this article, a highly accurate system model for detecting the diseases occurring in citrus fruits using a hybrid feature development approach is proposed. The overall improvement in terms of accuracy is measured and depicted.     

Size‐Optimized Ultrasmall Porous Silica Nanoparticles Depict Vasculature‐Based Differential Targeting in Triple Negative Breast Cancer

Rapid sequestration and prolonged retention of intravenously injected nanoparticles by the liver and spleen (reticuloendothelial system (RES)) presents a major barrier to effective delivery to the target site and hampers clinical translation of nanomedicine. Inspired by biological macromolecular drugs, synthesis of ultrasmall (diameter ≈12–15 nm) porous silica nanoparticles (UPSNs), capable of prolonged plasma half‐life, attenuated RES sequestration, and accelerated hepatobiliary clearance, is reported. The study further investigates the effect of tumor vascularization on uptake and retention of UPSNs in two mouse models of triple negative breast cancer with distinctly different microenvironments. A semimechanistic mathematical model is developed to gain mechanistic insights into the interactions between the UPSNs and the biological entities of interest, specifically the RES. Despite similar systemic pharmacokinetic profiles, UPSNs demonstrate strikingly different tumor responses in the two models. Histopathology confirms the differences in vasculature and stromal status of the two models, and corresponding differences in the microscopic distribution of UPSNs within the tumors. The studies demonstrate the successful application of multidisciplinary and complementary approaches, based on laboratory experimentation and mathematical modeling, to concurrently design optimized nanomaterials, and investigate their complex biological interactions, in order to drive innovation and translation.     

A position and rotation invariant framework for sign language recognition (SLR) using Kinect

Multimedia Tools and Applications - Sign language is the only means of communication for speech and hearing impaired people. Using machine translation, Sign Language Recognition (SLR) systems...