FORMULATION AND OPTIMIZATION OF ORODISPERSIBLE TABLETS OF IBUPROFEN

Objective: The present study aimed to formulate, develop and optimize orodispersible tablets of ibuprofen.Methods: Orodispersible tablets were prepared by direct compression technique using crospovidone, sodium starch glycolate, croscaramelose sodium, sodium carboxy methylcellulose as superdisintegrants at concentrations of 5, 7.5 and 10% w/w and mannitol used as diluent. The prepared powder mixtures are subjected to pre compression parameters including FTIR spectroscopy, DSC and micromeritics. The formulations were evaluated for tablet weight variation, hardness, friability, wetting time, absorption ratio, drug content, in vitro dispersion time, in vitro disintegration time and in vitro drug release studies.Results: The results of micromeritics studies revealed that all formulations were of acceptable to good flowability. Crospovidone at 10% w/w concentration (F3) showed the least in vitro disintegration time 38 seconds with acceptable hardness 3.93 kg/cm3, friability 0.652% and good dissolution profile (D5 min = 95.89%) in comparison with control (D5 min = 18.29%). The optimized formulation showed t90% drug release at 2.6 minutes. The FTIR and DSC studies were done for the optimized formula and showed no interaction between the drug and excipients.Conclusion: It is concluded that crospovidone gives the best results at 10% w/w (F3) for formulation of orodispersible tablets of ibuprofen with better pharmaceutical properties.Keywords: Orodispersible, Ibuprofen, Superdisintegrant, Crosspovidone, Direct compressio

Hybrid Signal Processing and Soft Computing approaches to Power System Frequency Estimation

Dynamic variation in power system frequency is required to be estimated for implementing the correcting measures. This paper presents power system frequency estimation by using RLS-Adaline and KF-Adaline algorithms. In the proposed hybrid approaches the weights of the Adaline are updated using RLS/KF algorithms. Frequency of power system signal is estimated from final updated weights of the Adaline. The performances of the proposed algorithms are studied through simulations for several critical cases that often arise in a power system. These studies show that the KF-Adaline algorithm is superior over the RLS-Adaline in estimating power system frequency. Studies made on experimental data also support the superiority

Forecasting Global Solar Insolation Using the Ensemble Kalman Filter Based Clearness Index Model

This paper describes a novel approach in developing a model for forecasting of global insolation on a horizontal plane. In the proposed forecasting model, constraints, such as latitude and whole precipitable water content in vertical column of that location, are used. These parameters can be easily measurable with a global positioning system (GPS). The earlier model was developed by using the above datasets generated from different locations in India. The model has been verified by calculating theoretical global insolation for different sites covering east, west, north, south and the central region with the measured values from the same locations. The model has also been validated on a region, from which data was not used during the development of the model. In the model, clearness index coefficients (KT) are updated using the ensemble Kalman filter (EnKF) algorithm. The forecasting efficacies using the KT model and EnKF algorithm have also been verified by comparing two popular algorithms, namely the recursive least square (RLS) and Kalman filter (KF) algorithms. The minimum mean absolute percentage error (MAPE), mean square error (MSE) and correlation coefficient (R) value obtained in global solar insolation estimations using EnKF in one of the locations are 2.4%, 0.0285 and 0.9866 respectively

Multiobjective Optimized Smart Charge Controller for Electric Vehicle Applications

The continuous deployment of distributed energy sources and the increase in the adoption of electric vehicles (EVs) require smart charging algorithms. The existing EV chargers offer limited flexibility and controllability and do not fully consider factors (such as EV user waiting time and the length of next trip) as well as the potential opportunities and financial benefits from using EVs to support the grid, charge from renewable energy, and deal with the negative impacts of intermittent renewable generation. The lack of adequate smart EV charging may result in high battery degradation, violation of grid control statutory limits, high greenhouse emissions, and high charging cost. In this article, a neuro-fuzzy particle swarm optimization (PSO)-based novel and advanced smart charge controller is proposed, which considers user requirements, energy tariff, grid condition (e.g., voltage or frequency), renewable (photovoltaic) output, and battery state of health. A rule-based fuzzy controller becomes complex as the number of inputs to the controller increases. In addition, it becomes difficult to achieve an optimum operation due to the conflicting nature of control requirements. To optimize the controller response, the PSO technique is proposed to provide a global optimum solution based on a predefined cost function, and to address the implementation complexity, PSO is combined with a neural network. The proposed neuro-fuzzy PSO control algorithm meets EV user requirements, works within technical constraints, and is simple to implement in real time (and requires less processing time). Simulation using MATLAB and experimental results using a dSPACE digital real-time emulator are presented to demonstrate the effectiveness of the proposed controller

Generalised Modelling, Simulation and Control Design for Manipulators with Flexible Links and Flexible/Rigid Joints

In this paper, a generalised Euler-Lagrange assumed modes for formulation for a manipulator with multiple flexible links and flexible joints is presented. The general modelling scheme is validated through computer simulations and analysis of the simplified single and two link manipulator models, with two different combinations of links and joints, namely first, flexible link (s) and rigid joint(s) and the second, flexible link(s) and flexible joint(s). To resolve the control complexity associated with the under-actuated flexible link and flexible joint manipulator, a singular perturbation model and control design for tracking are proposed with simultaneous suppression of the tip and joint vibrations

Efficient collision-free path planning for autonomous underwater vehicles in dynamic environments with a hybrid optimization algorithm

publisher: Elsevier articletitle: Efficient collision-free path planning for autonomous underwater vehicles in dynamic environments with a hybrid optimization algorithm journaltitle: Ocean Engineering articlelink: http://dx.doi.org/10.1016/j.oceaneng.2016.09.040 content_type: article copyright: © 2016 Elsevier Ltd. All rights reserved

Genetic variation in Southern USA rice genotypes for seedling salinity tolerance

The success of a rice breeding program in developing salt tolerant varieties depends on genetic variation and the salt stress response of adapted and donor rice germplasm. In this study, we used a combination of morphological and physiological traits in multivariate analyses to elucidate the phenotypic and genetic variation in salinity tolerance of thirty Southern USA rice genotypes, along with nineteen donor genotypes with varying degrees of tolerance. Significant genotypic variation and correlations were found among the salt injury score (SIS), ion leakage, chlorophyll reduction, shoot length reduction, shoot K+ concentration, and shoot Na+/K+ ratio. Using these parameters, the combined methods of cluster analysis and discriminant analysis validated the salinity response of known genotypes and classified most of the USA varieties into sensitive groups, except for three and seven varieties placed in the tolerant and moderately tolerant groups, respectively. Discriminant function and MANOVA delineated the differences in tolerance and suggested no differences between sensitive and highly sensitive groups. DNA profiling using simple sequence repeat markers showed narrow genetic diversity among USA genotypes. However, the overall genetic clustering was mostly due to subspecies and grain type differentiation and not by varietal grouping based on salinity tolerance. Among the donor genotypes, Nona Bokra, Pokkali, and its derived breeding lines remained the donors of choice for improving salinity tolerance during the seedling stage. However, due to undesirable agronomic attributes and photosensitivity of these donors, alternative genotypes such as TCCP266, Geumgangbyeo, and R609 are recommended as useful and novel sources of salinity tolerance for USA rice breeding programs

AltitudeOmics : Resetting of Cerebrovascular CO2 Reactivity Following Acclimatization to High Altitude.

Previous studies reported enhanced cerebrovascular CO2 reactivity upon ascent to high altitude using linear models. However, there is evidence that this response may be sigmoidal in nature. Moreover, it was speculated that these changes at high altitude are mediated by alterations in acid-base buffering. Accordingly, we reanalyzed previously published data to assess middle cerebral blood flow velocity (MCAv) responses to modified rebreathing at sea level (SL), upon ascent (ALT1) and following 16 days of acclimatization (ALT16) to 5260 m in 21 lowlanders. Using sigmoid curve fitting of the MCAv responses to CO2, we found the amplitude (95 vs. 129%, SL vs. ALT1, 95% confidence intervals (CI) [77, 112], [111, 145], respectively, P = 0.024) and the slope of the sigmoid response (4.5 vs. 7.5%/mmHg, SL vs. ALT1, 95% CIs [3.1, 5.9], [6.0, 9.0], respectively, P = 0.026) to be enhanced at ALT1, which persisted with acclimatization at ALT16 (amplitude: 177, 95% CI [139, 215], P < 0.001; slope: 10.3%/mmHg, 95% CI [8.2, 12.5], P = 0.003) compared to SL. Meanwhile, the sigmoidal response midpoint was unchanged at ALT1 (SL: 36.5 mmHg; ALT1: 35.4 mmHg, 95% CIs [34.0, 39.0], [33.1, 37.7], respectively, P = 0.982), while it was reduced by ~7 mmHg at ALT16 (28.6 mmHg, 95% CI [26.4, 30.8], P = 0.001 vs. SL), indicating leftward shift of the cerebrovascular CO2 response to a lower arterial partial pressure of CO2 (PaCO2) following acclimatization to altitude. Sigmoid fitting revealed a leftward shift in the midpoint of the cerebrovascular response curve which could not be observed with linear fitting. These findings demonstrate that there is resetting of the cerebrovascular CO2 reactivity operating point to a lower PaCO2 following acclimatization to high altitude. This cerebrovascular resetting is likely the result of an altered acid-base buffer status resulting from prolonged exposure to the severe hypocapnia associated with ventilatory acclimatization to high altitude

Heuristic Multi-Agent Control for Energy Management of Microgrids with Distributed Energy Sources

The increased integration of distributed Renewable Energy Sources (RESs) and adoption of Electric Vehicles (EVs) require appropriate control and management of energy sources and EV charging. This becomes critical at the distribution system level, especially at a microgrid (MG) level. This control is required not only to mitigate the negative impacts of intermittent generation from RESs but also to make better use of available energy, reduce carbon footprint, maximize the overall profit of microgrid and increase energy autonomy by effective utilization of battery storage. This paper proposes a heuristic multi-agent based decentralized energy management approach for grid-connected MG. The MG comprises of active (controlled) and passive (uncontrolled) electrical loads, a photovoltaic (PV) system, battery energy storage system (BESS) and a charging post for electric vehicles. The proposed approach is aimed at optimizing the use of local energy generation from photovoltaic and smart energy utilization to serve electrical loads and EV as well as maximizing MG profit. The aim of the energy management is to supply local consumption at minimum cost and less dependency on the main grid supply. Utilizing energy available from RESs (PV and BESS), customers satisfaction (fulfilling local demand), considering uncertainty of renewable generation and load consumption and also taking into account technical constraint are the main strengths of the presented framework. Performance of the proposed algorithm is investigated under different operating conditions and its efficacy is verified

A Novel Mutation of the NARROW LEAF 1 Gene Adversely Affects Plant Architecture in Rice (Oryza sativa L.)

Plant architecture is critical for enhancing the adaptability and productivity of crop plants. Mutants with an altered plant architecture allow researchers to elucidate the genetic network and the underlying mechanisms. In this study, we characterized a novel nal1 rice mutant with short height, small panicle, and narrow and thick deep green leaves that was identified from a cross between a rice cultivar and a weedy rice accession. Bulked segregant analysis coupled with genome re-sequencing and cosegregation analysis revealed that the overall mutant phenotype was caused by a 1395-bp deletion spanning over the last two exons including the transcriptional end site of the nal1 gene. This deletion resulted in chimeric transcripts involving nal1 and the adjacent gene, which were validated by a reference-guided assembly of transcripts followed by PCR amplification. A comparative transcriptome analysis of the mutant and the wild-type rice revealed 263 differentially expressed genes involved in cell division, cell expansion, photosynthesis, reproduction, and gibberellin (GA) and brassinosteroids (BR) signaling pathways, suggesting the important regulatory role of nal1. Our study indicated that nal1 controls plant architecture through the regulation of genes involved in the photosynthetic apparatus, cell cycle, and GA and BR signaling pathways