Testing viability of cross subsidy using time-variant price elasticities of industrial demand for electricity: Indian experience

Indian electric tariffs are characterized by very high rates for industrial and commercial classes to permit subsidized electric consumption by residential and agricultural customers. We investigate the viability of this policy using monthly data for 1997–2003 on electric consumption by a few large industrial customers under the aegis of a small distribution company in the state of Uttar Pradesh. For a given price/cost ratio, it can be shown that if the cross-subsidizing class’ electricity demand is sufficiently elastic, increasing the class’ rates fail to recover incremental cross-subsidy necessary to support additional revenues for subsidized classes. This suboptimality is tested by individually estimating time-variant price-elasticities of demand for these industrial customers using Box-Cox and linear regressions. We find that at least for some of these customers, cross-subsidy was suboptimal prior to as late as October 2001, when rates were changed following reforms.     

An investigation of the ordered DO19 phase formation in the Ti-Al system

For the Ti-rich, Ti-AI system, the- and- ₂ phase boundaries have been obtained through differential thermal analysis for alloy compositions ranging from 10.2 to 25.2 at% Al (6 to 16 wt% Al). It has been shown that in the above mentioned composition range, a metastable disordered-phase can be quenched in. This metastable then transforms to the ordered ₂(DO)₁₉ phase upon heating and/or isothermal ageing. It has also been observed that the kinetics of this ordering phenomenon is composition dependent.     

The inhibitive effect of extraneous ion addition on stress corrosion cracking of alpha-brass

The addition of ammonium chloride, cobalt sulphate, cesium sulphate and ethylene diamine to a Mattsson solution and the addition of ammonium sulphate to the Pugh solution have been found to produce an inhibitive effect on the stress corrosion cracking of alpha-brass in these solutions. Ammonium chloride and ammonium sulphate additions affect the tarnish formation on the specimen as well as the cracking pattern. Electrochemical polarization studies have also been carried out with these two additives. The observations have been explained in the light of adsorption of these ionic species at selected sites on the metal surface and their possible interference with the cathodic reactions.     

Electrothermal study of carbon loaded ethylene-vinylacetate copolymer

Temperature dependence of resistance of carbon black loaded ethylene-vinylacetate (EVA) copolymer was studied from ?100 to 250°C in a modified differential scanning calorimeter (DSC) apparatus. Both room temperature resistivity and positive temperature coefficient of resistance (PTC) effects can be correlated with carbon black parameters (structure, surface area, porosity). Room temperature resistivity and the degree of increase of resistivity, (ratio of maximum to minimum) progressively decrease with carbon black structure if occlusion of polymer by carbon black is considered in calculating the volume fraction. An integrated surface areastructure-porosity equivalence of the carbon black is derived by introducing factors proportional to the volume and porosity of the black used. Any black parameter that increases conductivity (loading, surface area, structure, porosity) decreases the PTC effect. A cable compounder can therefore minimize the undesirable impact of PTC by suitable choice of black-parameters. The necessary considerations for other uses, where PTC effect is used for microswitches, heaters, etc., will be just the opposite. The modified DSC method provides a quick scanning tool for determining the suitability of semicrystalline polymer recipes, either for cable or for any other material using the PTC effect.     

Text-dependent speaker verification using classical LBG,adaptive LBG and FCM vector quantization

An important task of speaker verification is to generate speaker specific models and match an input speaker’s utterance with these models. This paper focuses on comparing the performance of text dependent speaker verification system using Mel Frequency Cepstral Coefficients feature and different Vector Quantization (VQ) based speaker modelling techniques to generate the speaker specific models. Speaker-specific information is mainly represented by spectral features and using these features we have developed the model which serves as an important entity for determining the claimed identity of the speaker. In the modelling part, we used Linde, Buzo, Gray (LBG) VQ, proposed adaptive LBG VQ and Fuzzy C Means (FCM) VQ for generating speaker specific model. The experimental results that are performed on microphonic database shows that accuracy significantly depends on the size of the codebook in all VQ techniques, and on FCM VQ accuracy also depend on the value of learning parameter of the objective function. Experiment results shows that how the accuracy of speaker verification system is depend on different representations of the codebook, different size of codebook in VQ modelling techniques and learning parameter in FCM VQ.     

Green biosynthesis,characterisation and antimicrobial activities of silver nanoparticles using fruit extract of Solanum viarum

Green synthesis of nanoparticles is considered an efficient method when compared with chemical and physical methods because of its bulk production, eco‐friendliness and low cost norms. The present study reports, for the first time, green synthesis of silver nanoparticles (AgNPs) at room temperature using Solanum viarum fruit extract. The visual appearance of brownish colour with an absorption band at 450 nm, as detected by ultraviolet‐visible spectrophotometer analysis, confirmed the formation of AgNPs. X‐ray diffraction confirmed the AgNPs to be crystalline with a face‐centred lattice. The transmission electron microscopy‐energy dispersive X‐ray spectroscopy image showed the AgNPs are poly‐dispersed and are mostly spherical and oval in shape with particle size ranging from 2 to 40 nm. Furthermore, Fourier transform‐infrared spectra of the synthesised AgNPs confirmed the presence of phytoconstituents as a capping agent. The antimicrobial activity study showed that the AgNPs exhibited high microbial activity against Bacillus subtilis, Escherichia coli, Pseudomonas aeruginosa, Staphylococcus aureus susp. aureus, Aspergillus niger, and Candida albicans. The highest antimicrobial activity of AgNPs synthesised by S. viarum fruit extract was observed in P. aeruginosa, S. aureus susp. aureus and C. albicans with zone of inhibition, 26.67 mm.Inspec keywords: nanomedicine, antibacterial activity, X‐ray chemical analysis, nanoparticles, transmission electron microscopy, particle size, infrared spectra, microorganisms, X‐ray diffraction, Fourier transform spectra, ultraviolet spectra, scanning electron microscopy, visible spectra, nanofabricationOther keywords: green biosynthesis, antimicrobial activities, silver nanoparticles, green synthesis, physical methods, study reports, solanum viarum fruit, ultraviolet‐visible spectrophotometer analysis, high microbial activity, highest antimicrobial activity, s. viarum fruit, transmission electron microscopy, energy dispersive X‐ray spectroscopy image     

Fine-Grained Multivariate Time Series Anomaly Detection in IoT

Sensors produce a large amount of multivariate time series data to record the states of Internet of Things (IoT) systems. Multivariate time series timestamp anomaly detection (TSAD) can identify timestamps of attacks and malfunctions. However, it is necessary to determine which sensor or indicator is abnormal to facilitate a more detailed diagnosis, a process referred to as fine-grained anomaly detection (FGAD). Although further FGAD can be extended based on TSAD methods, existing works do not provide a quantitative evaluation, and the performance is unknown. Therefore, to tackle the FGAD problem, this paper first verifies that the TSAD methods achieve low performance when applied to the FGAD task directly because of the excessive fusion of features and the ignoring of the relationship’s dynamic changes between indicators. Accordingly, this paper proposes a multivariate time series fine-grained anomaly detection (MFGAD) framework. To avoid excessive fusion of features, MFGAD constructs two sub-models to independently identify the abnormal timestamp and abnormal indicator instead of a single model and then combines the two kinds of abnormal results to detect the fine-grained anomaly. Based on this framework, an algorithm based on Graph Attention Neural Network (GAT) and Attention Convolutional Long-Short Term Memory (A-ConvLSTM) is proposed, in which GAT learns temporal features of multiple indicators to detect abnormal timestamps and A-ConvLSTM captures the dynamic relationship between indicators to identify abnormal indicators. Extensive simulations on a real-world dataset demonstrate that the proposed algorithm can achieve a higher F1 score and hit rate than the extension of existing TSAD methods with the benefit of two independent sub-models for timestamp and indicator detection.     

Recommendation engine based on derived wisdom for more similar item neighbors

Information Systems and e-Business Management - Collaborative filtering (CF) is a popular and widely accepted recommendation technique. CF is an automated form of word-of-mouth communication...