Cybernetic modeling of adaptive prediction of environmental changes by microorganisms

Microorganisms exhibit varied regulatory strategies such as direct regulation, symmetric anticipatory regulation, asymmetric anticipatory regulation, etc. Current mathematical modeling frameworks for the growth of microorganisms either do not incorporate regulation or assume that the microorganisms utilize the direct regulation strategy. In the present study, we extend the cybernetic modeling framework to account for asymmetric anticipatory regulation strategy. The extended model accurately captures various experimental observations. We use the developed model to explore the fitness advantage provided by the asymmetric anticipatory regulation strategy and observe that the optimal extent of asymmetric regulation depends on the selective pressure that the microorganisms experience. We also explore the importance of timing the response in anticipatory regulation and find that there is an optimal time, dependent on the extent of asymmetric regulation, at which microorganisms should respond anticipatorily to maximize their fitness. We then discuss the advantages offered by the cybernetic modeling framework over other modeling frameworks in modeling the asymmetric anticipatory regulation strategy. (C) 2013 Published by Elsevier Inc.

Elastic Resources Framework in IaaS, preserving performance SLAs

Elasticity in cloud systems provides the flexibility to acquire and relinquish computing resources on demand. However, in current virtualized systems resource allocation is mostly static. Resources are allocated during VM instantiation and any change in workload leading to significant increase or decrease in resources is handled by VM migration. Hence, cloud users tend to characterize their workloads at a coarse grained level which potentially leads to under-utilized VM resources or under performing application. A more flexible and adaptive resource allocation mechanism would benefit variable workloads, such as those characterized by web servers. In this paper, we present an elastic resources framework for IaaS cloud layer that addresses this need. The framework provisions for application workload forecasting engine, that predicts at run-time the expected demand, which is input to the resource manager to modulate resource allocation based on the predicted demand. Based on the prediction errors, resources can be over-allocated or under-allocated as compared to the actual demand made by the application. Over-allocation leads to unused resources and under allocation could cause under performance. To strike a good trade-off between over-allocation and under-performance we derive an excess cost model. In this model excess resources allocated are captured as over-allocation cost and under-allocation is captured as a penalty cost for violating application service level agreement (SLA). Confidence interval for predicted workload is used to minimize this excess cost with minimal effect on SLA violations. An example case-study for an academic institute web server workload is presented. Using the confidence interval to minimize excess cost, we achieve significant reduction in resource allocation requirement while restricting application SLA violations to below 2-3%.

Vertically aligned ZnO nanorods on flexible substrates for multifunctional device applications: Easy and cost-effective route

Zinc oxide nanorods (ZnO NRs) have been synthesized on flexible substrates by adopting a new and novel three-step process. The as-grown ZnO NRs are vertically aligned and have excellent chemical stoichiometry between its constituents. The transmission electron microscopic studies show that these NR structures are single crystalline and grown along the < 001 > direction. The optical studies show that these nanostructures have a direct optical band gap of about 3.34 eV. Therefore, the proposed methodology for the synthesis of vertically aligned NRs on flexible sheets launches a new route in the development of low-cost flexible devices. (C) 2014 Elsevier B.V. All rights reserved.

Capacity of Gaussian Channels With Energy Harvesting and Processing Cost

Energy harvesting sensor nodes are gaining popularity due to their ability to improve the network life time and are becoming a preferred choice supporting green communication. In this paper, we focus on communicating reliably over an additive white Gaussian noise channel using such an energy harvesting sensor node. An important part of this paper involves appropriate modeling of energy harvesting, as done via various practical architectures. Our main result is the characterization of the Shannon capacity of the communication system. The key technical challenge involves dealing with the dynamic (and stochastic) nature of the (quadratic) cost of the input to the channel. As a corollary, we find close connections between the capacity achieving energy management policies and the queueing theoretic throughput optimal policies.

Low-Cost Cloud-Based Design of Smart Rural Energy Device in Microgrid: Perspective India An Energy-On-Demand Service for Rural India

In this paper we present a combination of technologies to provide an Energy-on-Demand (EoD) service to enable low cost innovation suitable for microgrid networks. The system is designed around the low cost and simple Rural Energy Device (RED) Box which in combination with Short Message Service (SMS) communication methodology serves as an elementary proxy for Smart meters which are typically used in urban settings. Further, customer behavior and familiarity in using such devices based on mobile experience has been incorporated into the design philosophy. Customers are incentivized to interact with the system thus providing valuable behavioral and usage data to the Utility Service Provider (USP). Data that is collected over time can be used by the USP for analytics envisioned by using remote computing services known as cloud computing service. Cloud computing allows for a sharing of computational resources at the virtual level across several networks. The customer-system interaction is facilitated by a third party Telecom Service provider (TSP). The approximate cost of the RED Box is envisaged to be under USD 10 on production scale.

A Low-Cost System for Measurement and Spectral Analysis of Motor Acoustic Noise

Workplace noise has become one of the major issues in industry not only because of workers’ health but also due to safety. Electric motors, particularly, inverter fed induction motors emit objectionably high levels of noise. This has led to the emergence of a research area, concerned with measurement and mitigation of the acoustic noise. This paper presents a lowcost option for measurement and spectral analysis of acoustic noise emitted by electric motors. The system consists of an electret microphone, amplifier and filter. It makes use of the windows sound card and associated software for data acquisition and analysis. The measurement system is calibrated using a professional sound level meter. Acoustic noise measurements are made on an induction motor drive using the proposed system as per relevant international standards. These measurements are seen to match closely with those of a professional meter.

Accurate prediction of interfacial residues in two-domain proteins using evolutionary information: Implications for three-dimensional modeling

With the preponderance of multidomain proteins in eukaryotic genomes, it is essential to recognize the constituent domains and their functions. Often function involves communications across the domain interfaces, and the knowledge of the interacting sites is essential to our understanding of the structure-function relationship. Using evolutionary information extracted from homologous domains in at least two diverse domain architectures (single and multidomain), we predict the interface residues corresponding to domains from the two-domain proteins. We also use information from the three-dimensional structures of individual domains of two-domain proteins to train naive Bayes classifier model to predict the interfacial residues. Our predictions are highly accurate (approximate to 85%) and specific (approximate to 95%) to the domain-domain interfaces. This method is specific to multidomain proteins which contain domains in at least more than one protein architectural context. Using predicted residues to constrain domain-domain interaction, rigid-body docking was able to provide us with accurate full-length protein structures with correct orientation of domains. We believe that these results can be of considerable interest toward rational protein and interaction design, apart from providing us with valuable information on the nature of interactions. Proteins 2014; 82:1219-1234. (c) 2013 Wiley Periodicals, Inc.

Cost-effective wear and oxidation resistant electrodeposited Ni-pumice coating

In the search for newer distributed phases that can be used in Ni-composite coatings, inexpensive and naturally available pumice has been identified as a potential candidate material. The composition of the pumice mineral as determined by Rietveld analysis shows the presence of corundum, quartz, mulllite, moganite and coesite phases. Pumice stone is crushed, ball-milled, dried and dispersed in a nickel sulfamate bath and Ni-pumice coatings are electrodeposited at different current densities and magnetic agitation speeds. Pumice particles are uniformly incorporated in the nickel matrix and Ni-pumice composite coatings with microhardness as high as 540 HK are obtained at the lowest applied current density. In the electrodeposited Ni-pumice coatings, the grain size of Ni increases with the applied current density. The overall intensity of texture development is slightly stronger for the Ni-pumice composite coating compared to plain Ni coating and the texture evolution is possibly not the strongest deciding factor for the enhanced properties of Ni-pumice coatings. The wear and oxidation resistances of Ni-pumice coating are commensurate with that of Ni-SiC coating electrodeposited under similar conditions. (C) 2014 Elsevier B.V. All rights reserved.

Role of Vertex Index in Substructure Identification and Activity Prediction: A Study on Antitubercular Activity of a Series of Acid Alkyl Ester Derivatives

Tuberculosis (TB) is a life threatening disease caused due to infection from Mycobacterium tuberculosis (Mtb). That most of the TB strains have become resistant to various existing drugs, development of effective novel drug candidates to combat this disease is a need of the day. In spite of intensive research world-wide, the success rate of discovering a new anti-TB drug is very poor. Therefore, novel drug discovery methods have to be tried. We have used a rule based computational method that utilizes a vertex index, named `distance exponent index (D-x)' (taken x = -4 here) for predicting anti-TB activity of a series of acid alkyl ester derivatives. The method is meant to identify activity related substructures from a series a compounds and predict activity of a compound on that basis. The high degree of successful prediction in the present study suggests that the said method may be useful in discovering effective anti-TB compound. It is also apparent that substructural approaches may be leveraged for wide purposes in computer-aided drug design.

Skip Decision and Reference Frame Selection for Low-Complexity H.264/AVC Surveillance Video Coding

H. 264/advanced video coding surveillance video encoders use the Skip mode specified by the standard to reduce bandwidth. They also use multiple frames as reference for motion-compensated prediction. In this paper, we propose two techniques to reduce the bandwidth and computational cost of static camera surveillance video encoders without affecting detection and recognition performance. A spatial sampler is proposed to sample pixels that are segmented using a Gaussian mixture model. Modified weight updates are derived for the parameters of the mixture model to reduce floating point computations. A storage pattern of the parameters in memory is also modified to improve cache performance. Skip selection is performed using the segmentation results of the sampled pixels. The second contribution is a low computational cost algorithm to choose the reference frames. The proposed reference frame selection algorithm reduces the cost of coding uncovered background regions. We also study the number of reference frames required to achieve good coding efficiency. Distortion over foreground pixels is measured to quantify the performance of the proposed techniques. Experimental results show bit rate savings of up to 94.5% over methods proposed in literature on video surveillance data sets. The proposed techniques also provide up to 74.5% reduction in compression complexity without increasing the distortion over the foreground regions in the video sequence.

TIME VARYING LINEAR PREDICTION USING SPARSITY CONSTRAINTS

Time-varying linear prediction has been studied in the context of speech signals, in which the auto-regressive (AR) coefficients of the system function are modeled as a linear combination of a set of known bases. Traditionally, least squares minimization is used for the estimation of model parameters of the system. Motivated by the sparse nature of the excitation signal for voiced sounds, we explore the time-varying linear prediction modeling of speech signals using sparsity constraints. Parameter estimation is posed as a 0-norm minimization problem. The re-weighted 1-norm minimization technique is used to estimate the model parameters. We show that for sparsely excited time-varying systems, the formulation models the underlying system function better than the least squares error minimization approach. Evaluation with synthetic and real speech examples show that the estimated model parameters track the formant trajectories closer than the least squares approach.

Wind speed prediction using spatio-temporal covariance

High wind poses a number of hazards in different areas such as structural safety, aviation, and wind energy-where low wind speed is also a concern, pollutant transport, to name a few. Therefore, usage of a good prediction tool for wind speed is necessary in these areas. Like many other natural processes, behavior of wind is also associated with considerable uncertainties stemming from different sources. Therefore, to develop a reliable prediction tool for wind speed, these uncertainties should be taken into account. In this work, we propose a probabilistic framework for prediction of wind speed from measured spatio-temporal data. The framework is based on decompositions of spatio-temporal covariance and simulation using these decompositions. A novel simulation method based on a tensor decomposition is used here in this context. The proposed framework is composed of a set of four modules, and the modules have flexibility to accommodate further modifications. This framework is applied on measured data on wind speed in Ireland. Both short-and long-term predictions are addressed.

Graphene on Paper: A Simple, Low-Cost Chemical Sensing Platform

Graphene layers have been transferred directly on to paper without any intermediate layers to yield G-paper. Resistive gas sensors have been fabricated using strips of G-paper. These sensors achieved a remarkable lower limit of detection of similar to 300 parts per trillion (ppt) for NO2, which is comparable to or better than those from other paper-based sensors. Ultraviolet exposure was found to dramatically reduce the recovery time and improve response times. G-paper sensors are also found to be robust against minor strain, which was also found to increase sensitivity. G-paper is expected to enable a simple and inexpensive low-cost flexible graphene platform

Crystal Structure and Prediction

The notion of structure is central to the subject of chemistry. This review traces the development of the idea of crystal structure since the time when a crystal structure could be determined from a three-dimensional diffraction pattern and assesses the feasibility of computationally predicting an unknown crystal structure of a given molecule. Crystal structure prediction is of considerable fundamental and applied importance, and its successful execution is by no means a solved problem. The ease of crystal structure determination today has resulted in the availability of large numbers of crystal structures of higher-energy polymorphs and pseudopolymorphs. These structural libraries lead to the concept of a crystal structure landscape. A crystal structure of a compound may accordingly be taken as a data point in such a landscape.

New single-source precursor for bismuth sulfide and its use as low-cost counter electrode material for dye-sensitized solar cells

One new homoleptic Bi(dtc)(3)] (1) (dtc = 4-hydroxypiperdine dithiocarbamate) has been synthesized and characterized by microanalysis, IR, UV-Vis, H-1 and C-13 spectroscopy and X-ray crystallography. The photoluminescence spectrum for the compound in DMSO solution was recorded. The crystal structure of 1 displayed distorted octahedral geometry around the Bi(III) center bonded through sulfur atoms of the dithiocarbamate ligands. TGA indicates that the compound decomposes to a Bi and Bi-S phase system. The Bi and Bi-S obtained from decomposition of the compound have been characterized by pXRD, EDAX and SEM. Solvothermal decomposition of 1 in the absence and presence of two different capping agents yielded three morphologically different Bi2S3 systems which were deployed as counter-electrode in dye-sensitized solar cells (DSSCs). (C) 2015 Elsevier B.V. All rights reserved.