TRANSIENT NATURAL CONVECTION FLOW IN A RECTANGULAR CAVITY FILLED WITH A POROUS MATERIAL WITH LOCALIZED HEATING FROM BELOW AND THERMAL STRATIFICATION

The transient natural convection flow with thermal stratification in a rectangular cavity filled with fluid saturated porous medium obeying Darcy's law has been studied. Prior to the time t* = 0, the flow in the cavity is assumed to be motionless and all four walls of the cavity are at the same constant temperature. At time t* = 0, the temperatures of the vertical walls are suddenly increased which vary linearly with the distance y and at the same time on the bottom wall an isothermal heat source is placed centrally. This sudden change in the wall temperatures gives rise to unsteadiness in the problem. The horizontal temperature difference induces and sustains a buoyancy driven flow in the cavity which is then controlled by the vertical temperature difference. The partial differential equations governing the transient natural convection flow have been solved numerically. The local and average Nusselt numbers decrease rapidly in a small time interval after the start of the impulsive change in the wall temperatures and the steady state is reached quickly. The time required to reach the steady state depends on the Rayleigh number and the thermal stratification parameter.

Hydrogen bonding, halogen bonding and lithium bonding: an atoms in molecules and natural bond orbital perspective towards conservation of total bond order, inter- and intra-molecular bonding

One hundred complexes have been investigated exhibiting D-X center dot center dot center dot A interactions, where X = H, Cl or Li and DX is the `X bond' donor and A is the acceptor. The optimized structures of all these complexes have been used to propose a generalized `Legon-Millen rule' for the angular geometry in all these interactions. A detailed Atoms in Molecules (AIM) theoretical analysis confirms an important conclusion, known in the literature: there is a strong correlation between the electron density at the X center dot center dot center dot A bond critical point (BCP) and the interaction energy for all these interactions. In addition, we show that extrapolation of the fitted line leads to the ionic bond for Li-bonding (electrostatic) while for hydrogen and chlorine bonding, it leads to the covalent bond. Further, we observe a strong correlation between the change in electron density at the D-X BCP and that at the X center dot center dot center dot A BCP, suggesting conservation of the bond order. The correlation found between penetration and electron density at BCP can be very useful for crystal structure analysis, which relies on arbitrary van der Waals radii for estimating penetration. Various criteria proposed for shared-and closed-shell interactions based on electron density topology have been tested for H/Cl/Li bonded complexes. Finally, using the natural bond orbital (NBO) analysis it is shown that the D-X bond weakens upon X bond formation, whether it is ionic (DLi) or covalent (DH/DCl) and the respective indices such as ionicity or covalent bond order decrease. Clearly, one can think of conservation of bond order that includes ionic and covalent contributions to both D-X and X center dot center dot center dot A bonds, for not only X = H/Cl/Li investigated here but also any atom involved in intermolecular bonding.

CLAP: A web-server for automatic classification of proteins with special reference to multi-domain proteins

Background: The function of a protein can be deciphered with higher accuracy from its structure than from its amino acid sequence. Due to the huge gap in the available protein sequence and structural space, tools that can generate functionally homogeneous clusters using only the sequence information, hold great importance. For this, traditional alignment-based tools work well in most cases and clustering is performed on the basis of sequence similarity. But, in the case of multi-domain proteins, the alignment quality might be poor due to varied lengths of the proteins, domain shuffling or circular permutations. Multi-domain proteins are ubiquitous in nature, hence alignment-free tools, which overcome the shortcomings of alignment-based protein comparison methods, are required. Further, existing tools classify proteins using only domain-level information and hence miss out on the information encoded in the tethered regions or accessory domains. Our method, on the other hand, takes into account the full-length sequence of a protein, consolidating the complete sequence information to understand a given protein better. Results: Our web-server, CLAP (Classification of Proteins), is one such alignment-free software for automatic classification of protein sequences. It utilizes a pattern-matching algorithm that assigns local matching scores (LMS) to residues that are a part of the matched patterns between two sequences being compared. CLAP works on full-length sequences and does not require prior domain definitions. Pilot studies undertaken previously on protein kinases and immunoglobulins have shown that CLAP yields clusters, which have high functional and domain architectural similarity. Moreover, parsing at a statistically determined cut-off resulted in clusters that corroborated with the sub-family level classification of that particular domain family. Conclusions: CLAP is a useful protein-clustering tool, independent of domain assignment, domain order, sequence length and domain diversity. Our method can be used for any set of protein sequences, yielding functionally relevant clusters with high domain architectural homogeneity. The CLAP web server is freely available for academic use at http://nslab.mbu.iisc.ernet.in/clap/.

VISUAL OBJECT TRACKING VIA RANDOM FERNS BASED CLASSIFICATION

Designing a robust algorithm for visual object tracking has been a challenging task since many years. There are trackers in the literature that are reasonably accurate for many tracking scenarios but most of them are computationally expensive. This narrows down their applicability as many tracking applications demand real time response. In this paper, we present a tracker based on random ferns. Tracking is posed as a classification problem and classification is done using ferns. We used ferns as they rely on binary features and are extremely fast at both training and classification as compared to other classification algorithms. Our experiments show that the proposed tracker performs well on some of the most challenging tracking datasets and executes much faster than one of the state-of-the-art trackers, without much difference in tracking accuracy.

A model for the classification and survey of clock synchronization protocols in WSNs

Clock synchronization in wireless sensor networks (WSNs) assures that sensor nodes have the same reference clock time. This is necessary not only for various WSN applications but also for many system level protocols for WSNs such as MAC protocols, and protocols for sleep scheduling of sensor nodes. Clock value of a node at a particular instant of time depends on its initial value and the frequency of the crystal oscillator used in the sensor node. The frequency of the crystal oscillator varies from node to node, and may also change over time depending upon many factors like temperature, humidity, etc. As a result, clock values of different sensor nodes diverge from each other and also from the real time clock, and hence, there is a requirement for clock synchronization in WSNs. Consequently, many clock synchronization protocols for WSNs have been proposed in the recent past. These protocols differ from each other considerably, and so, there is a need to understand them using a common platform. Towards this goal, this survey paper categorizes the features of clock synchronization protocols for WSNs into three types, viz, structural features, technical features, and global objective features. Each of these categories has different options to further segregate the features for better understanding. The features of clock synchronization protocols that have been used in this survey include all the features which have been used in existing surveys as well as new features such as how the clock value is propagated, when the clock value is propagated, and when the physical clock is updated, which are required for better understanding of the clock synchronization protocols in WSNs in a systematic way. This paper also gives a brief description of a few basic clock synchronization protocols for WSNs, and shows how these protocols fit into the above classification criteria. In addition, the recent clock synchronization protocols for WSNs, which are based on the above basic clock synchronization protocols, are also given alongside the corresponding basic clock synchronization protocols. Indeed, the proposed model for characterizing the clock synchronization protocols in WSNs can be used not only for analyzing the existing protocols but also for designing new clock synchronization protocols. (C) 2014 Elsevier B.V. All rights reserved.

ADAPTATION OF MW LEVEL NATURAL GAS LEAN BURN ENGINE FOR PRODUCER GAS OPERATION A GRID CONNECTED POWER PLANT

Availability of producer gas engines at MW being limited necessitates to adapt engine from natural gas operation. The present work focus on the development of necessary kit for adapting a 12 cylinder lean burn turbo-charged natural gas engine rated at 900 kWe (Waukesha make VHP5904LTD) to operate on producer and set up an appropriate capacity biomass gasification system for grid linked power generation in Thailand. The overall plant configuration had fuel processing, drying, reactor, cooling and cleaning system, water treatment, engine generator and power evacuation. The overall project is designed for evacuation of 1.5 MWe power to the state grid and had 2 gasification system with the above configuration and 3 engines. Two gasification system each designed for about 1100 kg/hr of woody biomass was connected to the engine using a producer gas carburetor for the necessary Air to fuel ratio control. In the use of PG to fuel IC engines, it has been recognized that the engine response will differ as compared to the response with conventional fueled operation due to the differences in the thermo-physical properties of PG. On fuelling a conventional engine with PG, power de-rating can be expected due to the lower calorific value (LCV), lower adiabatic flame temperature (AFT) and the lower than unity product to reactant more ratio. Further the A/F ratio for producer gas is about 1/10th that of natural gas and requires a different carburetor for engine operation. The research involved in developing a carburetor for varying load conditions. The patented carburetor is based on area ratio control, consisting of a zero pressure regulator and a separate gas and air line along with a mixing zone. The 95 litre engine at 1000 rpm has an electrical efficiency of 33.5 % with a heat input of 2.62 MW. Each engine had two carburetors designed for producer gas flow each capable of handling about 1200 m3/hr in order to provide similar engine heat input at a lower conversion efficiency. Cold flow studies simulating the engine carburetion system results showed that the A/F was maintained in the range of 1.3 +/- 0.1 over the entire flow range. Initially, the gasification system was tested using woody biomass and the gas composition was found to be CO 15 +/- 1.5 % H-2 22 +/- 2% CH4 2.2 +/- 0.5 CO2 11.25 +/- 1.4 % and rest N-2, with the calorific value in the range of 5.0 MJ/kg. After initial trials on the engine to fine tune the control system and adjust various engine operating parameter a peak load of 800 kWe was achieved, while a stable operating conditions was found to be at 750 kWe which is nearly 85 % of the natural gas rating. The specific fuel consumption was found to be 0.9 kg of biomass per kWh.

Exploring Technology Evolution Using Patent Classification: A Case of Cochlear Implant Technology Patents

Understanding technology evolution through periodic landscaping is an important stage of strategic planning in R&D Management. In fields like that of healthcare, where the initial R&D investment is huge and good medical product serve patients better, these activities become crucial. Approximately five percentage of the world population has hearing disabilities. Current hearing aid products meet less than ten percent of the global needs. Patent data and classifications on cochlear implants from 1977-2010, show the landscapes and evolution in the area of such implant. We attempt to highlight emergence and disappearance of patent classes over period of time showing variations in cochlear implant technologies. A network analysis technique is used to explore and capture technology evolution in patent classes showing what emerged or disappeared over time. Dominant classes are identified. The sporadic influence of university research in cochlear implants is also discussed.

Selective localisation of multi walled carbon nanotubes in polypropylene/natural rubber blends to reduce the percolation threshold

Polypropylene and natural rubber blends with multiwalled carbon nanotube (PP/NR + MWCNT nanocomposites) were prepared by melt mixing. The melt rheological behaviour of neat PP and PP/NR blends filled with different loadings (1, 3, 5, 7 wt%) of MWCNT was studied. The effect of PP/NR blends (with compositions, 80/20,50/50, 20/80 by wt) on the rheological percolation threshold was investigated. It was found that blending PP with NR (80/20 and 50/50 composition) reduced the rheological percolation threshold from 5 wt% to 3 wt% MWCNT. The melt rheological behaviour of the MWCNT filled PP/NR blends was correlated with the morphology observations from high resolution transmission electron microscopic (HRTEM) images. In predicting the thermodynamically favoured location of MWCNT in PP/NR blend, the specific interaction of phospholipids in NR phase with MWCNTs was considered quantitatively. The MWCNTs were selectively localised in the NR phase. The percolation mechanism in MWCNT filled PP/NR blends was discussed and for each blend composition, the percolation mechanism was found to be different. (C) 2015 Elsevier Ltd. All rights reserved.

A Simple Label Switching Algorithm for Semisupervised Structural SVMs

In structured output learning, obtaining labeled data for real-world applications is usually costly, while unlabeled examples are available in abundance. Semisupervised structured classification deals with a small number of labeled examples and a large number of unlabeled structured data. In this work, we consider semisupervised structural support vector machines with domain constraints. The optimization problem, which in general is not convex, contains the loss terms associated with the labeled and unlabeled examples, along with the domain constraints. We propose a simple optimization approach that alternates between solving a supervised learning problem and a constraint matching problem. Solving the constraint matching problem is difficult for structured prediction, and we propose an efficient and effective label switching method to solve it. The alternating optimization is carried out within a deterministic annealing framework, which helps in effective constraint matching and avoiding poor local minima, which are not very useful. The algorithm is simple and easy to implement. Further, it is suitable for any structured output learning problem where exact inference is available. Experiments on benchmark sequence labeling data sets and a natural language parsing data set show that the proposed approach, though simple, achieves comparable generalization performance.

Enantio sensing property of helicin, the derivative of a natural product: Discrimination of amines and amino alcohols

The chiral sensing property of helicin (the derivative of natural product obtained by partial oxidation of salicin, extracted from willow tree (Salix helix)) is reported. The use of helicin as a chiral derivatizing agent for the discrimination of amines and amino alcohols is convincingly established using H-1 NMR spectroscopy. The large chemical shift separation achieved between the discriminated peaks facilitated the accurate quantification of enantiomeric composition. The consistent trend observed in the shifting of imine proton peak (Delta delta) of helicin in all the derivatized molecules might aid the determination of spatial configuration. (C) 2015 Elsevier B.V. All rights reserved.

Enantio sensing property of helicin, the derivative of a natural product: Discrimination of amines and amino alcohols

The chiral sensing property of helicin (the derivative of natural product obtained by partial oxidation of salicin, extracted from willow tree (Salix helix)) is reported. The use of helicin as a chiral derivatizing agent for the discrimination of amines and amino alcohols is convincingly established using H-1 NMR spectroscopy. The large chemical shift separation achieved between the discriminated peaks facilitated the accurate quantification of enantiomeric composition. The consistent trend observed in the shifting of imine proton peak (Delta delta) of helicin in all the derivatized molecules might aid the determination of spatial configuration. (C) 2015 Elsevier B.V. All rights reserved.

Enantio sensing property of helicin, the derivative of a natural product: Discrimination of amines and amino alcohols

The chiral sensing property of helicin (the derivative of natural product obtained by partial oxidation of salicin, extracted from willow tree (Salix helix)) is reported. The use of helicin as a chiral derivatizing agent for the discrimination of amines and amino alcohols is convincingly established using H-1 NMR spectroscopy. The large chemical shift separation achieved between the discriminated peaks facilitated the accurate quantification of enantiomeric composition. The consistent trend observed in the shifting of imine proton peak (Delta delta) of helicin in all the derivatized molecules might aid the determination of spatial configuration. (C) 2015 Elsevier B.V. All rights reserved.

A Bacterial Foraging Optimization and Learning Automata Based Feature Selection for Motor Imagery EEG Classification

Selection of relevant features is an open problem in Brain-computer interfacing (BCI) research. Sometimes, features extracted from brain signals are high dimensional which in turn affects the accuracy of the classifier. Selection of the most relevant features improves the performance of the classifier and reduces the computational cost of the system. In this study, we have used a combination of Bacterial Foraging Optimization and Learning Automata to determine the best subset of features from a given motor imagery electroencephalography (EEG) based BCI dataset. Here, we have employed Discrete Wavelet Transform to obtain a high dimensional feature set and classified it by Distance Likelihood Ratio Test. Our proposed feature selector produced an accuracy of 80.291% in 216 seconds.