Search

In this paper, we derive the necessary conditions for time-energy optimal control of input-affine nonlinear systems using Hamilton–Jacobi–Bellman equation. The objective function is chosen as a weighted combination of control effort and time-horizon minimisation. The necessary conditions of optimality are transformed such that the resultant conditions are structurally similar to a differential matrix Riccati equation. A closed-form approximate solution to these necessary conditions is derived such that the resulting closed-loop dynamics is locally exponentially stable around the origin. Furthermore, it is also shown that the states converge within a ball of an arbitrarily small radius around the origin within a finite time. Finally, the performance of the proposed controller is tested using numerical simulations for Vander Pol's oscillator as well as spacecraft detumbling problem. Furthermore, the performance of the proposed controller is also tested in comparison to the infinite-horizon state-dependent Riccati equation controller. [ABSTRACT FROM AUTHOR]

The present study was designed to find a suitable microaerobic condition and ratio of glucose and xylose for maximum ethanol production using co-culture of Saccharomyces cerevisiae and Pichia stipitis. The maximum ethanol concentration and yield were achieved at 0.05 vvm aeration rate and 2:1 glucose/xylose ratio. The co-culture resulted in maximum ethanol concentration, ethanol yield, and volumetric productivity of 12.33 ± 0.10 g/L, 0.43 g/g, and 0.26 g/L/h, respectively. While, the monoculture of P. stipitis resulted in 8.96 ± 0.13 g/L, 0.36 g/g, and 0.19 g/L/h respectively. The fermentation carried out in microaerobic mode delivered 10.68% and 10.56% more ethanol concentration and ethanol yield respectively from glucose compared to the combination of anaerobic and microaerobic mode. Also, the glucose uptake rate increased to 0.83 g/L/h, which corresponds to an improvement of 50.16%, suggesting that the lower microaerophilic condition not only supports P. stipitis metabolism but also does S. cerevisiae to convert glucose faster in a co-culture system. Hence, co-culture cultivation in microaerobic mode would be a better condition to achieve maximum ethanol and productivity. [ABSTRACT FROM AUTHOR]

In the field of forestry studies, microwave remote sensing has broad applications due to the penetration into the semi-transparent media. This feature is used for the estimation of biophysical parameters and monitoring of deforestation. Therefore, the estimation of biophysical parameters is essential for assessing carbon stock management. Hence, the aboveground biomass (AGB) using synthetic aperture radar (SAR) data is recognized as typical approaches in forest application. However, the integrated use of polarimetric (PolSAR) and interferometric (PolInSAR) data might be more efficient tools for AGB mapping. Accordingly, in this study with the integrated data, the efficiency of machine learning techniques including random forest regression (RFR) and multiple linear regression (MLR) model were assessed and compared for the prediction of AGB. The analyses were performed using an image pair of fully polarimetric Radarsat-2 C-band data set and the related field data of Malhan Forest Range, Dehradun Forest Division, which were collected using the systematic sampling technique. Particularly, the training and testing of the models were done using the field sample plots. The experimental results showed that the RFR algorithm provided a better prediction result of AGB than the MLR model. The correlation coefficient (R2) and root-mean-square error (RMSE) for the RFR algorithm was estimated to be around 0.65 and 24.33 Mg/ha, respectively, while for the MLR model, R2 and RMSE are estimated as 0.54 and 33.05 Mg/ha, respectively. Therefore, it was concluded that the prediction of AGB through the machine learning technique using PolSAR and PolInSAR data has a significant advantage for accurate estimation of the AGB. [ABSTRACT FROM AUTHOR]

Scattering identification ambiguity has significant effects in the polarimetric SAR (Synthetic Aperture Radar) decomposition and can be reduced by discriminating the permanent and time-varying features using PolInSAR (Polarimetric SAR Interferometry) coherence. PolInSAR based polarimetric decomposition using cosine-squared distribution (PCSD decomposition) is an algorithm that demonstrates the incorporation of PolInSAR coherence in the decomposition model by assuming the distribution of forest structure and vegetation as cosine-squared. This study presents a multifrequency analysis of PCSD decomposition to evaluate its potential to rightly represent the scatterer by reducing the identification ambiguity. The false-color composites (FCC) of PCSD decomposed images are presented to examine the results. A comparison study is also conducted between the PCSD decomposition and the Yamaguchi-4 component (Y4C) decomposition to demonstrate the improvement in the representation of the ground targets. [ABSTRACT FROM AUTHOR]

Synthetic Aperture Radar (SAR) remote sensing is a state-of-the-art tool for snow monitoring and snow parameters estimation. SAR remote sensing-based techniques, such as interferometric SAR (InSAR) and Polarimetric SAR (PolSAR) have already proven useful in the estimation of geophysical parameters of snow. InSAR-based techniques utilize interferometric phase information from repeat-pass datasets for snow parameters retrieval. During the monitoring of snow, the large temporal gap between the repeat passes results in the temporal decorrelation in the snowpack, which leads to the loss of interferometric coherence. Hence, there is a need for a technique for snow parameters estimation, which can work with zero temporal baseline datasets. This study works on the development of a Polarimetric SAR Interferometry (PolInSAR) based modelling approach for snow-depth estimation using TerraSAR-X/TanDEM-X datasets acquired in the pursuit monostatic mode (temporal baseline = 10 seconds). The study area of this work is the Manali region of Himachal Pradesh situated in the Beas basin. Multi-temporal analysis of the snow-depth variation is executed utilizing the two pursuit-monostatic TanDEM-X interferometric quad-pol dataset pairs of the dates 21 January 2015 and 22 January 2015. In this study, the PolInSAR-based Coherence Amplitude Inversion modelling approach is used for the snow-depth retrieval. The magnitude of the complex interferometric coherence is used during the modelling implementation. The snow extinction coefficient is estimated and used as an input during PolInSAR modelling. Further, a comparison of the calculated volume coherence magnitude and the observed volume coherence magnitude is done during model implementation for the snow-depth estimation. The snow depth is estimated at a resolution of 15 m × 15 m in range and azimuth directions respectively. The estimated snow depth for both the dates shows a precise correlation with the ground datasets. The rise in model retrieved snow-depth value from 0.84 m to 1.24 m is observed during the period. The retrieved results were validated using the ground data of snow depth from the Automatic weather station (AWS) of Snow and Avalanche Study Establishment (SASE), Defence Research and Development Organization (DRDO), and Indian Institute of Remote Sensing (IIRS) installed in the Dhundi region of the study area for same dates. [ABSTRACT FROM AUTHOR]

The characterization of ground targets from a remotely sensed Synthetic Aperture Radar (SAR) image is addressed by polarimetric decomposition. The polarimetric SAR (PolSAR) decomposition measures the contribution of total backscatter from different scattering mechanisms using SAR images. The ambiguities present in the retrieval of scattering are the major problems associated with the model-based decomposition which could be reduced with a combination of interferometric coherence and PolSAR backscatter. The objective of this study is to improve the polarimetric decomposition model for identifying the scattering mechanisms based on the Polarimetric SAR Interferometry (PolInSAR) coherence for natural and manmade features. In this paper, we have proposed a model-based polarimetric decomposition using PolInSAR decorrelation. The PolInSAR decorrelation is exploited here to distinguish the time-varying and invariant scatterers present in the ground. The volume scattering power was calculated using the proposed decorrelation parameter which is the combination of PolInSAR coherence and decorrelation. The proposed algorithm has been tested on spaceborne multifrequency SAR datasets consisting of X-band TerraSAR-X and TerraSAR-X add-on for Digital Elevation Measurement (TanDEM-X), C-band Radarsat-2, and phased array L-band synthetic aperture radar-2 (PALSAR-2) data of advanced land observing satellite-2 (ALOS-2) PolInSAR data for the Dehradun region, India. The results show that there is a remarkable reduction in the ambiguities present in the identification of the scattering mechanism from the SAR image by using the proposed decorrelation-based decomposition model. Moreover, the algorithm is tested on X-band TerraSAR-X and TanDEM-X data of the Haridwar area and Rudrapur area, Uttarakhand, India to analyse the potential of the proposed decomposition technique in representing different manmade and natural features. [ABSTRACT FROM AUTHOR]

An efficient variant of the homotopy analysis method, namely the optimal homotopy analysis method (OHAM), has been presented for the solution of chemical process systems that are represented by the axial dispersion model. To show its efficiency OHAM has been successfully applied to one of the popular chemical engineering systems, i.e., axial dispersion model of a tubular chemical reactor sustaining nonlinear kinetics. The obtained optimal homotopy results have been shown to agree closely with the numerically obtained results. The usefulness of OHAM has further been illustrated by effectively capturing multiple solutions, which frequently arise in non-monotonic kinetics. Convergence of the so-obtained OHAM results has also been discussed. It is also worthwhile to mention that the presented methodology of OHAM, with slight modification, can easily be extended to other engineering systems not represented by the axial dispersion model. [ABSTRACT FROM AUTHOR]

In this study the exact analytical solution of the lumped parameter model of a nonlinear heat transfer process representing the transient convective-radiative cooling of a spherical body has been obtained. The process is governed by a nonlinear ordinary differential equation, and the exact analytical solution has been found in the implicit form of an elementary transcendental function. The obtained exact analytical solution not only yields accurate results but also successfully simulates a recent experimental study of cooling of metallic ball bearings by the combined mechanism of convection and radiation. In addition, the exact explicit solution for a simplified case of the above problem, recently tackled by several researchers in various approximate ways, has also been found. These exact solutions are quite appealing since they are accurate and superior to the available approximate solutions, provide better insight of the physical process, and can also serve as yardsticks for future testing of the approximate solutions. [ABSTRACT FROM AUTHOR]

Introduction: Tobacco-smoking is one of the most important risk factor for preterm delivery, pregnancy loss, low birth weight, and fetal growth restriction. It is estimated that approximately 30% of growth-restricted neonates could be independently associated with maternal smoking.Methods: In this study, gene expression profile, GSE11798, was chosen from GEO database with an aim to perceive change in gene expression signature in new born due to maternal smoking. Enrichment analysis was performed to annotate differentially expressed genes (DEGs) through gene ontology and pathway analysis using DAVID. Protein-protein interactions and module detection of these DEGs were carried out using cytoscape v3.6.0. Thirty umbilical cord tissue samples from 15 smokers and 15 non-smokers pregnant women were included in this analysis.Results: Twenty-six differentially expressed genes (DEGs) between two groups were selected using GEO2R tool. The DEGs were observed to be participating in biological processes/pathways related to growth releasing hormone, angiogenesis, embryonic skeletal, and cardiac development. Fibroblast growth factor receptor-1 (FGFR1) was identified to be the hub node with 348 interacting partners, which regulates transcription, cell growth, differentiation, and apoptosis. The up-regulation of FGFR1 in umbilical cord tissue may lead to reproductive and developmental complications such as encephalocraniocutaneous lipomatosis, osteoglophonic dysplasia, and Pfeiffer syndrome in new-borns.Conclusion: The findings manifests the possibility of overcoming these adverse health effects in new born through FGFR1 modulating treatments during pregnancy. [ABSTRACT FROM AUTHOR]

Municipal wastewater (WW), if not properly remediated, poses a threat to the environment and human health by carrying significant loads of nutrients and pathogens. These contaminants pollute rivers, lakes, and natural reservoirs where they cause eutrophication and pathogen-mediated diseases. However, the high nutrient content of WW makes it an ideal environment for remediation with microalgae that require high nutrient concentrations for growth and are not susceptible to toxins and pathogens. Given that an appropriate algal strain is used for remediation, the incurred biomass can be refined for the production of biofuel. Four microalgal species (Chlamydomonas reinhardtii, Chlorellasp.,Parachlorella kessleri-I, andNannochloropsis gaditana)were screened for efficient phycoremediation of municipal WW and potential use for biodiesel production. Among the four strains tested,P. kessleri-I showed the highest growth rate and biomass production in 100% WW. It efficiently removed all major nutrients with a removal rate of up to 98% for phosphate after 10 days of growth in 100% municipal WW collected from Delhi. The growth ofP. kessleri-I in WW resulted in a 50% increase of biomass and a 115% increase of lipid yield in comparison to growth in control media. The Fatty acid methyl ester (FAME), and fuel properties of lipids isolated from cells grown in WW complied with international standards. The present study provides evidence that the green algaP. kessleri-I effectively remediates municipal WW and can be used to produce biodiesel. [ABSTRACT FROM AUTHOR]