Change perception in complex auditory scenes

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Using Solar PV-Potentiated SRM Effort With Supply Power Control Functions

Electric vehicles(EVs) provide a workable solution to reduce greenhouse gas emissions and thus become a hot topic for research and development. SRM is one of the engines that enforce EV applications. To extend EV driving miles, the use of vehicle photovoltaic (PV) panels reduces vehicle battery reliability. Due to the characteristics of the SRM phase transformer, it is suggested in this article to convert three ports to control the energy flow between the PV panel and the battery and SRM devices. Six operating modes were introduced, four of which were developed for driving and two for loading at the station. In driving modes, the maximum power point monitoring (MPPT) for the PV panel and SRM speed control is performed. In station charging modes, the loaded charging network topology is developed without the need for external devices. When the PV panel recharges the battery directly, a multi-section charging strategy is used to power it. MATLAB / Simulink-based simulation results and experiments demonstrate the effectiveness of the proposed three-port switcher. This can have economic impacts on improving market acceptability

Landsat-8 Image Classification using Support Vector Machine Classifier

The extent of Built-up Area (BUA) is continuously increasing with rapid globalization. Identification of BUA provides vital information required for territorial planning as well as the impact of land cover changes on the environment. Therefore, detection of changes in land cover should be carried out periodically. However, it is difficult to extract built-up areas using satellite images because of the confusion between spectral values with other land cover types. Presently, satellite sensors provide continuous data in multiple spectral channels, which are becoming very useful for monitoring earth surface over large areas. The primary challenge is to accurately retrieve class information from the enormous set of data. The selected study area comprises of a scene taken from Haridwar District, India. The bounding coordinates of the chosen area are, long. 77 48’ 32.4’’ E and Lat. 29 54’ 50.4’’ N at upper left and long. 77 57’ 28.8’’ E and Lat. 29 45’ 46.8’’ N at lower right. In the last few decades, rapid urbanization has been taken place in this area, which results in increased infrastructural growth and urban expansion. The area mainly consists of land cover types such as built-up regions, agricultural land, water bodies, river sand and fallow land. The satellite data used in this study consists of multispectral bands acquired by Landsat-8 Operation Land Imager (OLI) sensor on 10 December 2014 with path- row number 146-39. The image represents a diverse land class scenario with 572 463 pixels in seven bands ranging from the wavelength of 0.43–2.29lm in the spectrum and having a spatial resolution of 30 m. In this study, medium resolution Landsat-8 data is used because it is suitable for mapping of land cover classes such as built-up area. However, the conventional methods are failed to provide the accurate classification performance. So, this work considered the machine learning based Support Vector Machine (SVM) classifier for obtaining the labelled samples from Landsat-8 Image

Landsat-8 Image Classification using Support Vector Machine Classifier

The extent of Built-up Area (BUA) is continuously increasing with rapid globalization. Identification of BUA provides vital information required for territorial planning as well as the impact of land cover changes on the environment. Therefore, detection of changes in land cover should be carried out periodically. However, it is difficult to extract built-up areas using satellite images because of the confusion between spectral values with other land cover types. Presently, satellite sensors provide continuous data in multiple spectral channels, which are becoming very useful for monitoring earth surface over large areas. The primary challenge is to accurately retrieve class information from the enormous set of data. The selected study area comprises of a scene taken from Haridwar District, India. The bounding coordinates of the chosen area are, long. 77 48’ 32.4’’ E and Lat. 29 54’ 50.4’’ N at upper left and long. 77 57’ 28.8’’ E and Lat. 29 45’ 46.8’’ N at lower right. In the last few decades, rapid urbanization has been taken place in this area, which results in increased infrastructural growth and urban expansion. The area mainly consists of land cover types such as built-up regions, agricultural land, water bodies, river sand and fallow land. The satellite data used in this study consists of multispectral bands acquired by Landsat-8 Operation Land Imager (OLI) sensor on 10 December 2014 with path- row number 146-39. The image represents a diverse land class scenario with 572 463 pixels in seven bands ranging from the wavelength of 0.43–2.29lm in the spectrum and having a spatial resolution of 30 m. In this study, medium resolution Landsat-8 data is used because it is suitable for mapping of land cover classes such as built-up area. However, the conventional methods are failed to provide the accurate classification performance. So, this work considered the machine learning based Support Vector Machine (SVM) classifier for obtaining the labelled samples from Landsat-8 Image

Application of functional networks in geotechnical engineering

In nature spatial variability of the soil is inevitable. The analysis of such unpredictable material only on the basis of experimental, finite element method and other traditionally available methods is reliable, but overall modeling based on these methods makes it more complex and this problem necessitated the usage of statistical models to develop some empirical and semi empirical methods with the obtained input and output data. Many statistical methods came from the past outperforming one another. Since the efficiency of certain tool also depends on the data chosen, the developed models though showed good results poor generalization was observed for some of the complex problems. Functional networks introduced by Castillo as an alternative to artificial neural network (ANN), in which functions are learned instead of weights, and also the functions are random chosen, unlike ANN they are constrained to certain functions. The selection of topology depends on both domain knowledge i.e. associativity, commutatively and others, where as ANN is a black box which blindly access the data by increasing the weights (trial and error process) The objective of this study is to show how functional network can be effectively used to model certain problems in geotechnical engineering. In this thesis four examples are considered under study (1) Prediction of lateral load capacity of piles in clay, (2) Prediction of factor of safety of slope, (3) Uplift capacity of suction caisson in clay, (4) Swelling pressure in clays, and the results are analyzed based on certain criterion like correlation coefficient, root mean square error, efficiency, cumulative probability distribution function. The observed results are also compared with other statistical methods like ANN, SVM, MGGP, etc and it was observed that FN almost added a rung over all those methods and this shows that this method can be better used in every aspect of geotechnical engineerin

A STUDY OF METHOD DEVELOPMENT, VALIDATION AND FORCED DEGRADATION FOR SIMULTANEOUS QUANTIFICATION OF CABOZANTINIB AND NIVOLUMAB IN BULK AND PHARMACEUTICAL DOSAGE FORM BY RP-HPLC

Objective: The present paper describes a simple, accurate, and precise reversed-phase high-performance liquid chromatography (HPLC) method for rapid and simultaneous quantification of cabozantinib (CZT) and nivolumab (NVM) in bulk and pharmaceutical dosage form. Methods: The chromatographic separation was achieved on Luna C18 (150 mm×4.6 mm, 3.5 μm). Mobile phase contained a mixture of 0.1% orthophosphoric acid and acetonitrile in the ratio of 50:50 v/v, flow rate 1.0 ml/min, and ultraviolet detection at 222 nm. Results: The proposed method shows a good linearity in the concentration range of 20–300 μg/ml for CZT and 5–75 μg/ml for NVM under optimized conditions. Precision and recovery study results are in between 98 and 102%. In the entire robustness conditions, percentage relative standard deviation is <2.0%. Degradation has minimum effect in stress condition and solutions are stable up to 24 h. Conclusion: This method is validated for different parameters such as precision, linearity, accuracy, limit of detection (LOD), limit of quantification (LOQ), ruggedness, robustness, and forced degradation study were determined according to the International Conference of Harmonization (ICH) Q2B guidelines. All the parameters of validation were found to be within the acceptance range of ICH guidelines. Since there is no HPLC method reported in the literature for the estimation of CZT and NVM in pharmaceutical dosage forms, there is a need to develop quantitative methods under different conditions to achieve improvement in sensitivity, selectivity, etc. The author declares the interest to develop a validation and forced degradation for simultaneous quantification of CZT and NVM

Semantic Web-based Turmeric Expert System using IWD Algorithm

Semantic web is a structured way of re-usable data representation that can be used for inferring new knowledge. It provides a common data format for data representation. And it also provides semantics through structured information. Agriculture involves a vast variety of unstructured information. Normally agricultural experts, with their vast experience, provide critical advice in their farming activity. Machine learning algorithms acquire knowledge in the same manner as that of a human expert acquires knowledge with experience. In the present paper, an expert advisory system is simulated using machine learning algorithm for providing expert advice to the end users. A critical study is conducted for understanding the semantic web stack and an attempt has been made to design and develop an expert system namely, "Semantic Web-based Turmeric Expert System using IWD Algorithm". The proposed system has two modules namely, expert advice module and information system module. The Advisory system takes certain details from the end users, regarding their crop and provides the suitable advice. In the present paper, only yield assessment module was considered. Yield estimation system uses Intelligent Water Drops (IWD) algorithm to estimate the yield for each crop variety. Information system provides information about Turmeric crop varieties, parts, pests, pesticides, symptoms and diseases. Protégé is used to develop Ontology. JENA framework is used to retrieve information from Ontology. SWRL rules are implemented to infer rules from the data

PURIFICATION AND MOLECULAR WEIGHT DETERMINATION OF KERATINASE ISOLATED FROM STREPTOMYCES MALAYSIENSIS

Objective: The aim of the present study was to purify and determine the molecular weight of keratinase isolated from Streptomyces malaysiensis.Methods: For that purpose purification was done using ammonium sulphate and Sephadex-LH 100 column chromatography. Further, the fractions were pooled and subjected to molecular weight determination using sodium dodecyl sulphate-polyacrylamide gel electrophoresis (SDS-PAGE).Results: The obtained results showed keratinase with 47.57% recovery, 3.5-fold purification and an estimated molecular mass of 27,000 Da. Keratinase showed an optimal activity at 60 οC and pH 8. Keratinase activity of the purified product was assayed with feather powder as a substrate. The isolated strain was identified as Streptomyces malaysiensis based on phylogenetic tree analysis. The strain isolated from termite mound soil showed the highest keratinase activity, which could be considered a microorganism of environmental origin.Conclusion: The production of keratinase on simple media with feathers as sole source allowing its production from the cheap substrate and a commercial production with low production cost. Stability in the presence of detergents, surfactants and solvents make this keratinase extremely useful for a biotechnological process involving keratin