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.     

A 150 MHz 0.6 μm BiCMOS superscalar microprocessor

An implementation of the Pentium microprocessor architecture in 0.6 μm BiCMOS technology is described. Power dissipation is reduced and performance is enhanced over the previous generation. Processor features, implementation technology, and circuit techniques are discussed. An internal clock rate of 150 MHz is achieved at 3.7 V and -55°C     

Role of heat transfer and thermal conductivity in the crystallization behavior of polypropylene‐containing additives: A phenomenological model

The thermal conductivity of a filler and the thermal conductivity of a composite made from that filler influence the heat‐transfer process during melt processing. The heat‐transfer process from the melt to the mold wall becomes an important factor in developing the skin–core morphology. These aspects were examined in this study. The thermal conductivity of polypropylene–filler composites was estimated with a standard model for various fillers such as calcium carbonate, talc, silica, wollastonite, mica, and carbon fibers. The rate of cooling under given conditions, including the melting temperature, mold wall temperature, mass of the composite, and filler content, was estimated with standard heat‐transfer equations. The time to attain the crystallization temperature for polypropylene was evaluated with a regression method with differential temperature steps. The crystallization curves were experimentally determined for the different fillers, and from them, the induction period for the onset of crystallization was estimated. These observations were correlated with the expected trends from the aforementioned formalism. The excellent fit of the curves showed that in all these cases, the thermal conductivity of the filler and composite played a dominant role in controlling the onset of the crystallization process. However, the nucleation effects became important in the later stages after the crystallization temperature was attained. © 2003 Wiley Periodicals, Inc. J Appl Polym Sci 89: 2994–2999, 2003     

Effect of thiophosphoryl compounds as accelerators and coupling agents on the vulcanization of NR‐XNBR blend

The effect of a number of thiophosphoryl compounds [viz., bis(isobutyl) thiophosphoryl di‐, tri‐, and tetrasulfides and bis(dicyclohexyl) thiophopsphoryl di‐, tri‐, and tetrasulfides] on natural rubber (NR)‐carboxylated nitrile rubber (XNBR) blend was studied. All these thiophosphoryl compounds are capable of forming interrubber links leading to covulcanized blends which exhibit a fair degree of synergism with respect to physical properties, the maximum being obtained at the 25% NR and 75% XNBR blend composition. This is an obvious claim that the blend under investigation is technologically compatible, having some degree of interrubber interaction which is enhanced in case of two‐stage vulcanization. The existence of interrubber interaction is judged by the swelling experiment. The blend morphology assessed by SEM micrographs corroborates the foregoing observations and accounts for the significant improvement in physical properties of the blend vulcanizates, particularly in two‐stage vulcanization. © 2002 Wiley Periodicals, Inc. J Appl Polym Sci 86: 3286‐3299, 2002     

Developing a Dynamic Cluster Quantization based Lossless Audio Compression (DCQLAC)

In this paper, an approach has been made to produce a compressed audio without losing any information. The proposed scheme is fabricated with the help of dynamic cluster quantization followed by Burrows Wheeler Transform (BWT) and Huffman coding. The encoding algorithm has been designed in two phases, i.e., dynamic cluster selection (of sampled audio) followed by dynamic bit selection for determining quantization level of individual cluster. Quantization level of each cluster is selected dynamically based on mean square quantization error (MSQE). Bit stream is further compressed by applying Burrows Wheeler Transform (BWT) and Huffman code respectively. Experimental results are supported with current state-of-the-art in audio quality analysis (like statistical parameters (compression ratio, space savings, SNR, PSNR) along with other parameters (encoding time, decoding time, Mean Opinion Score (MOS) and entropy) and compared with other existing techniques.

     

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.     

Taming the killer in the kitchen: mitigating household air pollution from solid-fuel cookstoves through building design

In this study, we attempt to mitigate household air pollution (HAP) through improved kitchen design. Field surveys were conducted in ten kitchens of rural western India, which were then modelled and simulated for dynamic indoor airflow network analysis. The simulated results were statistically clustered using principal component analysis and hierarchical agglomerative clustering, to construct a cumulative built environment parameter called ‘Built Factor’ for each kitchen, and subsequently a derivative matrix was developed. Categorization of better performing kitchens from this derivative matrix enabled in deriving the built parameter thresholds for a ‘better’ kitchen design. This derived kitchen showed 60 % reduction in PM2.5 peak concentration during cooking hours. The evaluation described here is essentially a “proof of concept”, that effective building design can be an alternative way to reduce HAP without the introduction of chimneys, improved cookstoves or shifting to cleaner fuel.     

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.