Thermally stimulated discharge current studies in pure and fe-doped polystyrene films

Studies were made on the thermally stimulated discharge currents (TSDCs) in pure (undoped) and Fe-doped polystyrene films as a function of polarizing field, polarizing temperature and dopant concentration. While undoped films exhibited a single peak, doped films showed two peaks one at low temperatures and another at high temperatures. The low temperature peak, which exhibits a shift towards lower temperatures with increasing dopant concentration, is attributed to the relaxation of the main chain, while the high temperature peak, which shows a tendency to shift towards higher temperatures with dopant concentration, is due to space charge polarization. The TSDCs were higher for low dopant concentrations than their undoped counterparts, while for high concentrations of the dopant, the TSDCs decreased. Formation of charge transfer complexes at low dopant concentrations and molecular aggregates at higher dopant concentrations are suggested as the possible reasons for this behaviour.     

Instrumental characterization of clay by XRF,XRD and FTIR

Instrumental characterizations of the clay were performed by different techniques such as XRF, XRD and FTIR. XRF shows the chemical compositions of the clay where Al-oxide and silica oxide are present in major quantity whereas XRD confirms the presence of these minerals in clay. FTIR studies show the presence of quartz, alumina, haematite and different mineral matters.     

Measurement of AC conductivity and dielectric properties of flexible conductive styrene–butadiene rubber‐carbon black composites

Flexible conductive polymer composites were prepared using styrene–butadiene rubber (SBR) as a matrix and conductive carbon black as filler. The filler loading was varied from 10 to 60 phr. The effect of frequency, filler loading, temperature, and applied pressure on the AC conductivity, permittivity, and loss factor of the composites was studied. The AC conductivity of low and high loaded composites was found to be frequency dependent and independent respectively. The permittivity and the loss factor were continuously decreasing with increasing frequency. The increase in filler loading increased the AC conductivity, dielectric constant, and loss factor of the composites. Increase in temperature imposed increase in conductivity and permittivity of the composites. With increasing applied pressure the properties showed exponential increase. The effect of time under a constant compressive stress was studied and dielectric relaxation times were evaluated. © 2007 Wiley Periodicals, Inc. J Appl Polym Sci 104: 986–995, 2007     

Requirement-oriented risk management for incremental software development

In incremental software development (ISD) functionalities are delivered incrementally and requirements keep on evolving across iterations. The requirements evolution involves the addition of new dependencies and conflicts among functional and non-functional requirements along with changes in priorities and dependency weights. This, in turn, demands refactoring the order of development of system components to minimize the impact of these changes. Neglecting the non-functional constraints in the software development process exposes it to risks that may accumulate across several iterations. In this research work, we propose a risk management framework for ISD processes that provides an estimate of risk exposure for the project when functional features are frozen while ignoring the associations with non-functional requirements. Our framework proposes suitable risk reduction strategies that work in tandem with the risk assessment module. We also provide a tool interface for our risk management framework.

     

(BaTiO3)1-x + (Co0.5Ni0.5Nb0.06Fe1.94O4)x nanocomposites: Structure,morphology, magnetic and dielectric properties

Two phase-based nanocomposites consisting of dielectric barium titanate (BaTiO₃ or BTO) and magnetic spinel ferrite Co_0.5Ni_0.5Nb_0.06Fe_1.94O₄ (CNNFO) have been synthesized through solid state route. Series of (BaTiO₃)_1-x + (Co_0.5Ni_0.5Nb_0.06Fe_1.94O₄)_x nanocomposites with x content of 0.00, 0.25, 0.50, 0.75, and 1.00 were considered. The structure has been examined via X-rays diffraction (XRD) and indicated the occurrence of both perovskite BTO and spinel CNNFO phases in various nanocomposites. A phase transition from tetragonal BTO structure to cubic structure occurs with inclusion of CNNFO phase. The average crystallites size of BTO phase decreases, whereas that for the CNNFO phase increases with increasing x in various nanocomposites. The morphological observations revealed that the porosity is highly reduced, and the connectivity between grains is enhanced with increasing x content. The optical properties have been investigated by UV−vis diffuse reflectance spectroscopy. The deduced band gap energy (E_g) value is found to reduce with increasing the content of spinel ferrite phase. The magnetic as well as the dielectric properties were also investigated. The analysis showed that CNNFO ferrite phase greatly affects the magnetic properties and dielectric response of BTO material. The obtained findings can be useful to enhance the performances of magneto-dielectric composite-based systems.     

On the electrochemical nature of hot-corrosion attack in Ni-Cr alloys

Hot-corrosion attack on gas-turbine components has been a problem of practical importance for over two decades. The nature and the extent of corrosion are influenced by the type of contaminants, the alloy constituents, and temperature. The hot-corrosion process is quite complex. There is controversy regarding the role of alloying elements, and whether the process is chemical or electrochemical in nature. In this investigation, an attempt has been made to investigate the role of Al and Ti in influencing the hot-corrosion attack of Ni-20 wt. % Cr alloys in molten Na₂SO₄-NaCl mixtures. It was found that Ni-Cr-Al alloys undergo a localized, heavy-blister attack under certain experimental conditions which is electrochemical in nature. An electrochemical model has been proposed which can satisfatorily explain the nature and kinetics of the localized blister corrosion.     

Verification of evolving software via component substitutability analysis

This paper presents an automated and compositional procedure to solve the substitutability problem in the context of evolving software systems. Our solution contributes two techniques for checking correctness of software upgrades: (1) a technique based on simultaneous use of over-and under-approximations obtained via existential and universal abstractions; (2) a dynamic assume-guarantee reasoning algorithm—previously generated component assumptions are reused and altered on-the-fly to prove or disprove the global safety properties on the updated system. When upgrades are found to be non-substitutable, our solution generates constructive feedback to developers showing how to improve the components. The substitutability approach has been implemented and validated in the ComFoRT reasoning framework, and we report encouraging results on an industrial benchmark. This is an extended version of a paper, Dynamic Component Substitutability Analysis, published in the Proceedings of the Formal Methods 2005 Conference, Lecture Notes in Computer Science, vol. 3582, by the same authors. This research was sponsored by the National Science Foundation under grant nos. CNS-0411152, CCF-0429120, CCR-0121547, and CCR-0098072, the Semiconductor Research Corporation under grant no. TJ-1366, the US Army Research Office under grant no. DAAD19-01-1-0485, the Office of Naval Research under grant no. N00014-01-1-0796, the ICAST project and the Predictable Assembly from Certifiable Components (PACC) initiative at the Software Engineering Institute, Carnegie Mellon University. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of any sponsoring institution, the US government or any other entity.     

FSE2R: An Improved Collision-Avoidance-based Energy Efficient Route Selection Protocol in USN

The 3D Underwater Sensor Network (USNs) has become the most optimistic medium for tracking and monitoring underwater environment. Energy and collision are two most critical factors in USNs for both sparse and dense regions. Due to harsh ocean environment, it is a challenge to design a reliable energy efficient with collision free protocol. Diversity in link qualities may cause collision and frequent communication lead to energy loss; that effects the network performance. To overcome these challenges a novel protocol Forwarder Selection Energy Efficient Routing (FSE2R) is proposed. Our proposal’s key idea is based on computation of node distance from the sink, Residual Energy (RE) of each node and Signal to Interference Noise Ratio (SINR). The node distance from sink and RE is computed for reliable forwarder node selection and SINR is used for analysis of collision. The novel proposal compares with existing protocols like H2AB, DEEP, and E2LR to achieve Quality of Service (QoS) in terms of throughput, packet delivery ratio and energy consumption. The comparative analysis shows that FSE2R gives on an average 30% less energy consumption, 24.62% better PDR and 48.31% less end-to-end delay compared to other protocols.     

Sentiment Analysis with Tweets Behaviour in Twitter Streaming API

Twitter is a radiant platform with a quick and effective technique to analyze users’ perceptions of activities on social media. Many researchers and industry experts show their attention to Twitter sentiment analysis to recognize the stakeholder group. The sentiment analysis needs an advanced level of approaches including adoption to encompass data sentiment analysis and various machine learning tools. An assessment of sentiment analysis in multiple fields that affect their elevations among the people in real-time by using Naive Bayes and Support Vector Machine (SVM). This paper focused on analysing the distinguished sentiment techniques in tweets behaviour datasets for various spheres such as healthcare, behaviour estimation, etc. In addition, the results in this work explore and validate the statistical machine learning classifiers that provide the accuracy percentages attained in terms of positive, negative and neutral tweets. In this work, we obligated Twitter Application Programming Interface (API) account and programmed in python for sentiment analysis approach for the computational measure of user’s perceptions that extract a massive number of tweets and provide market value to the Twitter account proprietor. To distinguish the results in terms of the performance evaluation, an error analysis investigates the features of various stakeholders comprising social media analytics researchers, Natural Language Processing (NLP) developers, engineering managers and experts involved to have a decision-making approach.     

Trends in intelligent manufacturing research: a keyword co-occurrence network based review

Journal of Intelligent Manufacturing - In recent years, driven by Industry 4.0 wave, academic research has focused on the science, engineering, and enabling technologies for intelligent and cyber...