Synthesis of new metal-containing side-chain monomers and polymers

New metal-containing vinyl monomers, hexyl-6-oxy-{4-[4-(4-carboxy cyclopentadienyl manganese tricarbonyl phenyl)phenyl]benzoyloxy}methacrylate and hexyl-6-oxy-{4-[4-(4-ferrocenoyl phenyl)phenyl]benzoyloxy}methacrylate, and the corresponding homopolymers and random copolymers with hydroxy monomer hexyl-6-oxy-{4-[4-(4-hydroxyphenyl)phenyl]benzoyloxy}methacrylate were synthesized. The compounds were characterized by¹H NMR; their thermal behavior was investigated by means of differential scanning calorimetry. Monomers and polymers containing the ferrocene unit melt at lower temperatures than those derived from the cyclopentadienyl managanese tricarbonyl moiety. The melting temperatures of the monomers and polymers ranged from 399 to about 515 K, Both monomers and polymers failed to exhibit mesogenic behavior. Values ofM _n,M _w,M _w/M _n, and degree of polymerization were obtained by gel permeation chromatography. TheM _n ranged from 16,500 for the copolymer containing hexyl-6-oxy-{4-[4-(4-ferrocenoyl phenyl)phenyl] benzoyloxy}methacrylate and hydroxy monomer hexyl-6-oxy-{4-[4-(4-hydroxyphenyl)phenyl]benzoyloxy}methacrylate at a 1:3 ratio to 26,000 for the copolymer containing hexyl-6-oxy-{4-[4-(4-carboxy cyclopentadienyl manganese tricarbonyl phenyl)phenyl]benzoyloxy}methacrylate and hydroxy monomer hexyl-6-oxy-{4-[4-(4-hydroxyphenyl)phenyl]benzoyloxy}methacrylate at a 1:3 ratio.M _w/M _n ranged from 1.6 in the case of the copolymer containing hexyl-6-oxy-{4-[4-(4-carboxy cyclopentadienyl manganese tricarbonyl phenyl)phenyl]benzoyloxy}methacrylate and hydroxy monomer hexyl-6-oxy-{4-[4-(4-hydroxyphenyl)phenyl]benzoyloxy}methacrylate at a 1:3 ratio to 2.2 in the case of poly(hexyl-6-oxy{4-[4-(4-carboxy cyclopentadienyl manganese tricarbonyl phenyl)phenyl]benzoyloxy}methacrylate).     

Identification of Indian languages using multi-level spectral and prosodic features

In this paper spectral and prosodic features extracted from different levels are explored for analyzing the language specific information present in speech. In this work, spectral features extracted from frames of 20 ms (block processing), individual pitch cycles (pitch synchronous analysis) and glottal closure regions are used for discriminating the languages. Prosodic features extracted from syllable, tri-syllable and multi-word (phrase) levels are proposed in addition to spectral features for capturing the language specific information. In this study, language specific prosody is represented by intonation, rhythm and stress features at syllable and tri-syllable (words) levels, whereas temporal variations in fundamental frequency (F ₀ contour), durations of syllables and temporal variations in intensities (energy contour) are used to represent the prosody at multi-word (phrase) level. For analyzing the language specific information in the proposed features, Indian language speech database (IITKGP-MLILSC) is used. Gaussian mixture models are used to capture the language specific information from the proposed features. The evaluation results indicate that language identification performance is improved with combination of features. Performance of proposed features is also analyzed on standard Oregon Graduate Institute Multi-Language Telephone-based Speech (OGI-MLTS) database.     

Precise shape analysis using field sensitivity

We present a static shape analysis technique to infer the shapes of the heap structures created by a program at run time. Our technique is field sensitive in that it uses field information to compute the shapes. The shapes of the heap structures are computed using two components: (a) Boolean functions that capture the shape transitions due to the update of a field in a structure, and (b) through path matrices that store approximate path information between two pointer variables. We classify the shapes as one of Tree, Directed Acyclic Graph (DAG) and Cycle. The novelty of our approach lies in the way we use field information to remember the fields that cause a heap structure to have a particular shape (Tree, DAG or Cycle). This allows us to easily identify the field updates that cause shape transitions from Cycle to DAG, from Cycle to Tree and from DAG to Tree. This makes our analysis more precise as compared to earlier shape analyses that ignore the fields participating in the formation of a shape. We implemented our analysis in GCC as a dynamic plug-in as an interprocedural data-flow analysis and evaluated it on some standard benchmarks against a field-insensitive shape analysis technique as a baseline approach. We are able to achieve significant precision as compared to the baseline analysis (at the cost of increase in analysis time). In particular, we are able to infer more precise shapes for 4 out 7 Olden benchmarks, and never detect more cycles than the baseline analysis. We further suggest enhancements to improve the precision of our analysis under some constraints and to improve the analysis time at the cost of precision.     

A hybrid MPI–OpenMP scheme for scalable parallel pseudospectral computations for fluid turbulence

A hybrid scheme that utilizes MPI for distributed memory parallelism and OpenMP for shared memory parallelism is presented. The work is motivated by the desire to achieve exceptionally high Reynolds numbers in pseudospectral computations of fluid turbulence on emerging petascale, high core-count, massively parallel processing systems. The hybrid implementation derives from and augments a well-tested scalable MPI-parallelized pseudospectral code. The hybrid paradigm leads to a new picture for the domain decomposition of the pseudospectral grids, which is helpful in understanding, among other things, the 3D transpose of the global data that is necessary for the parallel fast Fourier transforms that are the central component of the numerical discretizations. Details of the hybrid implementation are provided, and performance tests illustrate the utility of the method. It is shown that the hybrid scheme achieves good scalability up to ～20,000 compute cores with a maximum efficiency of 89%, and a mean of 79%. Data are presented that help guide the choice of the optimal number of MPI tasks and OpenMP threads in order to maximize code performance on two different platforms.     

A fast quasi-Newton method for semi-supervised SVM

Due to its wide applicability, semi-supervised learning is an attractive method for using unlabeled data in classification. In this work, we present a semi-supervised support vector classifier that is designed using quasi-Newton method for nonsmooth convex functions. The proposed algorithm is suitable in dealing with very large number of examples and features. Numerical experiments on various benchmark datasets showed that the proposed algorithm is fast and gives improved generalization performance over the existing methods. Further, a non-linear semi-supervised SVM has been proposed based on a multiple label switching scheme. This non-linear semi-supervised SVM is found to converge faster and it is found to improve generalization performance on several benchmark datasets.     

Self-Healing Concrete with Crystalline Admixture——A Review

The scope of the present paper is to understand the effects of crystalline admixture on the selfhealing capacity of the cementitious composites. Previous studies were examined and a conclusion was drawn to the effect that different additives to crystalline admixture tend to improve self-healing of concrete for larger cracks. It is recommended that initial treatment with chemical admixture can stimulate and heal further cracks and it has the better repeatability trend in mixing with the concretes and mechanical recovery is possible even under repetitive preloading. Effective self-healing with chemical admixtures even under open-air exposure, leads to study the importance of a service ability design parameter including the maximum allowable crack width by repeatability analysis as a function of the exposure with the concept of sealable crack width.     

Selection of an optimal parametric combination for achieving a better surface finish in dry milling using genetic algorithms

In machining, coolants improve machinability, increase productivity by reducing tool wear and extend tool life. However, due to ecological and human health problems, manufacturing industries are now being forced to implement strategies to reduce the amount of cutting fluids used in their production lines. A trend that has emerged to solve these problems is machining without fluid – a method called dry machining – which has been made possible due to technological innovations. This paper presents an experimental investigation of the influence of tool geometry (radial rake angle and nose radius) and cutting conditions (cutting speed and feed rate) on machining performance in dry milling with four fluted solid TiAlN-coated carbide end mill cutters based on Taguchi’s experimental design method. The mathematical model, in terms of machining parameters, was developed for surface roughness prediction using response surface methodology. The optimization is then carried out with genetic algorithms using the surface roughness model developed and validated in this work. This methodology helps to determine the best possible tool geometry and cutting conditions for dry milling.     

High resolution resistive thermometry for micro∕nanoscale measurements

High resolution thermometry plays an important role in several micro∕nanoscale studies. Here, we present a detailed analysis of the resolution of resistance thermometry schemes that employ an electrical sensing current to monitor the temperature-dependent resistance. Specifically, we theoretically and experimentally analyze four different schemes where modulated or unmodulated temperatures in microdevices are measured using modulated or unmodulated sensing currents. Our analysis and experiments suggest that measurement of unmodulated temperatures using a modulated sensing current improves the resolution in comparison to a scenario where an unmodulated sensing current is used. However, depending on the exact measurement conditions, such improvements might be modest as the overall resolution may be limited by random low frequency environmental temperature fluctuations. More importantly, we find that high-resolution thermometry can be achieved in the measurement of modulated temperatures. Specifically, we show that by using appropriate instrumentation and a 10 k[ohm sign] platinum resistance thermometer it is possible to measure modulated temperatures (0.5-20 Hz) with a resolution of about 20-100 μK. The advances described here will enable a dramatic improvement in the heat-current resolution of resistive thermometry based microdevices that are used for probing nanoscale phonon and photon transport.     

Toxic effects of disyston and furadan on the bimodal pattern of oxygen consumption in the climbing perch, Anabas testudineus (Bloch)

A significant change was noted in the aquatic (vO₂), aerial (VO₂) and total [(v + V)O₂] consumption of Anabas testudineus (Bloch) exposed to 4.0 and 10.5 mg l⁻¹ disyston and 0.56 and 1.56 mg l⁻¹ furadan. After 1 h of exposure to all the concentrations significant decreases were noticed in the (v + V)O₂. Though significant increases were noted in all the measures of respiration after 3 h of exposure in both the pesticides, their effects vary from one exposure period to another. The fish held in 4.0 mg l⁻¹ disyston and 0.56 mg l⁻¹ furadan concentrations resumed normal pattern of bimodal respiration after 120 and 96 h respectively. Further the pattern of changes in (v + V)O₂ closely followed the changes observed in VO₂ rather than vO₂ indicating that the fish held in pesticide medium predominantly relied on aerial respiration. A direct correlation was noticed between the VO₂ consumption and the surfacing frequency of the fish exposed to disyston and furadan. Relatively furadan is more toxic than disyston to Anabas testudineus if their effects on O₂ consumption are taken into consideration.