COMPARISON AND CROSS-VALIDATION OF OPTICAL TECHNIQUES IN DIFFERENT SWIRL SPRAY REGIMES

This paper deals with an experimental study of pressure-swirl hydraulic injector nozzles using non-intrusive optical techniques. Experiments were conducted to study atomization characteristics using two nozzles with different orifice diameters, 0.3 mm and 0.5 mm, and injection pressures, 0.3-3.5 Mpa, which correspond to Reynolds number (Re-p) = 7,000-45,000, depending on nozzle utilized. Three laser diagnostic techniques were utilized: Shadowgraph, PIV (Particle Image Velocimetry), and PDPA (Phase Doppler Particle Anemometry). Measurements made in the spray in both axial and radial directions indicate that velocity, average droplet diameter profiles, and spray dynamics are highly dependent on the nozzle characteristics and injection pressure. Limitations of these techniques in the different flow regimes, related to the primary and secondary breakups as well as coalescence, are provided. Results indicate that all three techniques provide similar results throughout the different regimes. Shadowgraph and PDPA were possible in the secondary atomization and coalescence regimes while PIV measurements could be made only at the end of secondary atomization and coalescence.

Classification of Myopathies on Molecular basis in Drosophila using Raman spectroscopy

Myopathies are muscular diseases in which muscle fibers degenerate due to many factors such as nutrient deficiency, infection and mutations in myofibrillar etc. The objective of this study is to identify the bio-markers to distinguish various muscle mutants in Drosophila (fruit fly) using Raman Spectroscopy. Principal Components based Linear Discriminant Analysis (PC-LDA) classification model yielding >95% accuracy was developed to classify such different mutants representing various myopathies according to their physiopathology.

Pd-Catalyzed Cross-Coupling Reactions of Hydrazones: Regioselective Synthesis of Highly Branched Dienes

The regioselective formation of highly branched dienes is a challenging task. Design and exploration of alternative working models to achieve such a regioselectivity to accomplish highly branched dienes is considered to be a historical advancement of Heck reaction to construct branched dienes. On the basis of the utility of carbene transfer reactions, in the reaction of hydrazones with Pd(II) under oxidative conditions, we envisioned obtaining a Pd-bis-carbene complex with alpha-hydrogens, which can lead to branched dienes. Herein, we report a novel Pd-catalyzed selective coupling reaction of hydrazones in the presence of t-BuOLi and benzoquinone to form the corresponding branched dienes. The utility of the Pd catalyst for the cross-coupling reactions for synthesizing branched conjugated dienes is rare. The reaction is very versatile and compatible with a variety of functional groups and is useful in synthesizing heterocyclic molecules. We anticipate that this Pd-catalyzed cross-coupling reaction will open new avenues for synthesizing useful compounds.

Cross-Hetero-Dehydrogenative Coupling Reaction of Phosphites: A Catalytic Metal-Free Phosphorylation of Amines and Alcohols

Phosphorylation of amines, alcohols, and sulfoximines are accomplished using molecular iodine as a catalyst and H2O2 as the sole oxidant under mild reaction conditions. This method provides an easy route for synthesizing a variety of phosphoramidates, phosphorus triesters and sulfoximine-derived phosphoramidates which are of biological importance.

H.264 COMPRESSED VIDEO CLASSIFICATION USING HISTOGRAM OF ORIENTED MOTION VECTORS (HOMV)

In this paper, we have proposed a simple and effective approach to classify H.264 compressed videos, by capturing orientation information from the motion vectors. Our major contribution involves computing Histogram of Oriented Motion Vectors (HOMV) for overlapping hierarchical Space-Time cubes. The Space-Time cubes selected are partially overlapped. HOMV is found to be very effective to define the motion characteristics of these cubes. We then use Bag of Features (B OF) approach to define the video as histogram of HOMV keywords, obtained using k-means clustering. The video feature, thus computed, is found to be very effective in classifying videos. We demonstrate our results with experiments on two large publicly available video database.

A NEAR OPTIMAL PROJECTION FOR SPARSE REPRESENTATION BASED CLASSIFICATION

Sparse representation based classification (SRC) is one of the most successful methods that has been developed in recent times for face recognition. Optimal projection for Sparse representation based classification (OPSRC)1] provides a dimensionality reduction map that is supposed to give optimum performance for SRC framework. However, the computational complexity involved in this method is too high. Here, we propose a new projection technique using the data scatter matrix which is computationally superior to the optimal projection method with comparable classification accuracy with respect OPSRC. The performance of the proposed approach is benchmarked with various publicly available face database.

Closed-form solutions for axially functionally graded Timoshenko beams having uniform cross-section and fixed-fixed boundary condition

In this paper, we study the free vibration of axially functionally graded (AFG) Timoshenko beams, with uniform cross-section and having fixed-fixed boundary condition. For certain polynomial variations of the material mass density, elastic modulus and shear modulus, along the length of the beam, there exists a fundamental closed form solution to the coupled second order governing differential equations with variable coefficients. It is found that there are an infinite number of non-homogeneous Timoshenko beams, with various material mass density, elastic modulus and shear modulus distributions having simple polynomial variations, which share the same fundamental frequency. The derived results can be used as benchmark solutions for testing approximate or numerical methods used for the vibration analysis of non-homogeneous Timoshenko beams. They can also be useful for designing fixed-fixed non-homogeneous Timoshenko beams which may be required to vibrate with a particular frequency. (C) 2013 Elsevier Ltd. All rights reserved.

Cross-Linked, Biodegradable, Cytocompatible Salicylic Acid Based Polyesters for Localized, Sustained Delivery of Salicylic Acid: An In Vitro Study

In order to suppress chronic inflammation while supporting cell proliferation, there has been a continuous surge toward development of polymers with the intention of delivering anti-inflammatory molecules in a sustained manner. In the above backdrop, we report the synthesis of a novel, stable, cross-linked polyester with salicylic acid (SA) incorporated in the polymeric backbone and propose a simple synthesis route by melt condensation. The as-synthesized polymer was hydrophobic with a glass transition temperature of 1 degrees C, which increases to 17 degrees C upon curing. The combination of NMR and FT-IR spectral techniques established the ester linkages in the as-synthesized SA-based polyester. The pH-dependent degradation rate and the rate of release of salicylic acid from the as-synthesized SA-based polymer were studied at physiological conditions in vitro. The polyester underwent surface erosion and exhibited linear degradation kinetics in which a change in degradation rate is observed after 4-10 days and 24% mass loss was recorded after 4 months at 37 degrees C and pH 7.4. The delivery of salicylic acid also showed a similar change in slopes, with a sustained release rate of 3.5% in 4 months. The cytocompatibility studies of these polyesters were carried out with C2C12 murine myoblast cells using techniques like MTT assay and flow cytometry. Our results strongly suggest that SA-based polyester supports cell proliferation for 3 days in culture and do not cause cell death (<7%), as quantified by propidium iodide (PI) stained cells. Hence, these polyesters can be used as implant materials for localized, sustained delivery of salicylic acid and have applications in adjuvant cancer therapy, chronic wound healing, and as an alternative to commercially available polymers like poly(lactic acid) and poly(glycolic acid) or their copolymers.

Generative Maximum Entropy Learning for Multiclass Classification

Maximum entropy approach to classification is very well studied in applied statistics and machine learning and almost all the methods that exists in literature are discriminative in nature. In this paper, we introduce a maximum entropy classification method with feature selection for large dimensional data such as text datasets that is generative in nature. To tackle the curse of dimensionality of large data sets, we employ conditional independence assumption (Naive Bayes) and we perform feature selection simultaneously, by enforcing a `maximum discrimination' between estimated class conditional densities. For two class problems, in the proposed method, we use Jeffreys (J) divergence to discriminate the class conditional densities. To extend our method to the multi-class case, we propose a completely new approach by considering a multi-distribution divergence: we replace Jeffreys divergence by Jensen-Shannon (JS) divergence to discriminate conditional densities of multiple classes. In order to reduce computational complexity, we employ a modified Jensen-Shannon divergence (JS(GM)), based on AM-GM inequality. We show that the resulting divergence is a natural generalization of Jeffreys divergence to a multiple distributions case. As far as the theoretical justifications are concerned we show that when one intends to select the best features in a generative maximum entropy approach, maximum discrimination using J-divergence emerges naturally in binary classification. Performance and comparative study of the proposed algorithms have been demonstrated on large dimensional text and gene expression datasets that show our methods scale up very well with large dimensional datasets.

Large-Scale Elastic Net Regularized Linear Classification SVMs and Logistic Regression

Elastic Net Regularizers have shown much promise in designing sparse classifiers for linear classification. In this work, we propose an alternating optimization approach to solve the dual problems of elastic net regularized linear classification Support Vector Machines (SVMs) and logistic regression (LR). One of the sub-problems turns out to be a simple projection. The other sub-problem can be solved using dual coordinate descent methods developed for non-sparse L2-regularized linear SVMs and LR, without altering their iteration complexity and convergence properties. Experiments on very large datasets indicate that the proposed dual coordinate descent - projection (DCD-P) methods are fast and achieve comparable generalization performance after the first pass through the data, with extremely sparse models.

The relationship between classification of multi-domain proteins using an alignment-free approach and their functions: a case study with immunoglobulins

Establishing functional relationships between multi-domain protein sequences is a non-trivial task. Traditionally, delineating functional assignment and relationships of proteins requires domain assignments as a prerequisite. This process is sensitive to alignment quality and domain definitions. In multi-domain proteins due to multiple reasons, the quality of alignments is poor. We report the correspondence between the classification of proteins represented as full-length gene products and their functions. Our approach differs fundamentally from traditional methods in not performing the classification at the level of domains. Our method is based on an alignment free local matching scores (LMS) computation at the amino-acid sequence level followed by hierarchical clustering. As there are no gold standards for full-length protein sequence classification, we resorted to Gene Ontology and domain-architecture based similarity measures to assess our classification. The final clusters obtained using LMS show high functional and domain architectural similarities. Comparison of the current method with alignment based approaches at both domain and full-length protein showed superiority of the LMS scores. Using this method we have recreated objective relationships among different protein kinase sub-families and also classified immunoglobulin containing proteins where sub-family definitions do not exist currently. This method can be applied to any set of protein sequences and hence will be instrumental in analysis of large numbers of full-length protein sequences.

Two new atom centered fragment descriptors and scoring function enhance classification of antibacterial activity

Classification of pharmacologic activity of a chemical compound is an essential step in any drug discovery process. We develop two new atom-centered fragment descriptors (vertex indices) - one based solely on topological considerations without discriminating atomor bond types, and another based on topological and electronic features. We also assess their usefulness by devising a method to rank and classify molecules with regard to their antibacterial activity. Classification performances of our method are found to be superior compared to two previous studies on large heterogeneous data sets for hit finding and hit-to-lead studies even though we use much fewer parameters. It is found that for hit finding studies topological features (simple graph) alone provide significant discriminating power, and for hit-to-lead process small but consistent improvement can be made by additionally including electronic features (colored graph). Our approach is simple, interpretable, and suitable for design of molecules as we do not use any physicochemical properties. The singular use of vertex index as descriptor, novel range based feature extraction, and rigorous statistical validation are the key elements of this study.

Multi Label Classification of Discrete Data

The paper describes an algorithm for multi-label classification. Since a pattern can belong to more than one class, the task of classifying a test pattern is a challenging one. We propose a new algorithm to carry out multi-label classification which works for discrete data. We have implemented the algorithm and presented the results for different multi-label data sets. The results have been compared with the algorithm multi-label KNN or ML-KNN and found to give good results.

Fuzzy Classification of Time Series Data

The problem of classification of time series data is an interesting problem in the field of data mining. Even though several algorithms have been proposed for the problem of time series classification we have developed an innovative algorithm which is computationally fast and accurate in several cases when compared with 1NN classifier. In our method we are calculating the fuzzy membership of each test pattern to be classified to each class. We have experimented with 6 benchmark datasets and compared our method with 1NN classifier.