Multilayer Structured Rectangular Microstrip Antenna for ISM Band Applications

In this paper efforts have been made to design and simulate the Multilayer Structured Rectangular Microstrip antenna for ISM Band applications. The shape will provide the bandwidth which is required in various wireless applications like Bluetooth (2.4 GHz-2.484 GHz), RFID (2.4 GHz - 2.5 GHz), and WLAN (3.6 GHz) etc. Coaxial feed technique is used for its simplicity. The performance of the designed antenna is analyzed in terms of Bandwidth, Return loss, Gain, VSWR, Directivity and Radiation Pattern. FR – 4 epoxy substrate has been used, which has dielectric constant of 4.4. DOI: 10.17762/ijritcc2321-8169.150311

Godan: A Study of Social Realism

With the publication of Godan, Premchand surmounted the summit of success. Godan spurs great amount introspection, which is crucial in order to come to terms with such disturbing themes as travails of the soul of the poor Indian farmers and abject poverty. The inclusion of these problems imparts a universal appeal to his novels. Godan is not a tale of misery but it is also a realistic representation of rural India. The novelist indicts Sanskritised Brahminical religion. The main thrust of Godan is to arouse the humanitarian attitude in man so that society learns to move on the path of socialism and a happy world. The novel presents a synthesis of art and society, man and his age. The novelist has expressed his views on religion, caste, social set-up and community in realistic manner

Experimental studies in antisolvent crystallization: Effect of antisolvent ratio and mixing patterns

18-25The crystals size and distribution play an important role in drug properties which has a major impact on the performance e.g., stability, solubility and bioavailability. The crystal size distribution (CSD) depends on the hydrodynamics and local degree of supersaturation in the crystallizer. In this study, we have investigated the effects of various operating conditions (antisolvent ratio, power, agitator design) using different mixing techniques such as impellers and ultrasound on CSD and average crystal size (ACS). It is found that mixing plays a dominant role in CSD and ACS. The hydrofoil (axial flow impeller) provides a wide range of ACS (406 to 240 μm) at lower power as compared to Rushton turbine (radial flow impeller) (395 to 375 μm). The mixed flow impeller produces the intermediate crystal size (365 to 345 μm). The increase in the antisolvent ratio results in a decrease in ACS. The same results observed for the power input

Discovering stochastic partial differential equations from limited data using variational Bayes inference

We propose a novel framework for discovering Stochastic Partial Differential Equations (SPDEs) from data. The proposed approach combines the concepts of stochastic calculus, variational Bayes theory, and sparse learning. We propose the extended Kramers-Moyal expansion to express the drift and diffusion terms of an SPDE in terms of state responses and use Spike-and-Slab priors with sparse learning techniques to efficiently and accurately discover the underlying SPDEs. The proposed approach has been applied to three canonical SPDEs, (a) stochastic heat equation, (b) stochastic Allen-Cahn equation, and (c) stochastic Nagumo equation. Our results demonstrate that the proposed approach can accurately identify the underlying SPDEs with limited data. This is the first attempt at discovering SPDEs from data, and it has significant implications for various scientific applications, such as climate modeling, financial forecasting, and chemical kinetics

MAntRA: A framework for model agnostic reliability analysis

We propose a novel model agnostic data-driven reliability analysis framework for time-dependent reliability analysis. The proposed approach -- referred to as MAntRA -- combines interpretable machine learning, Bayesian statistics, and identifying stochastic dynamic equation to evaluate reliability of stochastically-excited dynamical systems for which the governing physics is \textit{apriori} unknown. A two-stage approach is adopted: in the first stage, an efficient variational Bayesian equation discovery algorithm is developed to determine the governing physics of an underlying stochastic differential equation (SDE) from measured output data. The developed algorithm is efficient and accounts for epistemic uncertainty due to limited and noisy data, and aleatoric uncertainty because of environmental effect and external excitation. In the second stage, the discovered SDE is solved using a stochastic integration scheme and the probability failure is computed. The efficacy of the proposed approach is illustrated on three numerical examples. The results obtained indicate the possible application of the proposed approach for reliability analysis of in-situ and heritage structures from on-site measurements

Ayurveda and medicalisation today: The loss of important knowledge and practice in health?

Ayurveda translates as 'life science'. Its knowledge is not limited to medicine, cure or therapy and is for laypersons, households, communities, as well as for physicians. Throughout its evolutionary history, Ayurveda and Local Health Traditions have reciprocally influenced each other. In modern times, the influence of biomedicine on Ayurveda is leading to its medicalisation. Over the past century, the introduction and perspective of biomedicine into India has made the human being an object for positive knowledge, a being who can be understood with scientific reason and can be governed and controlled through medical knowledge. This paper explores how this shift towards medicalisation is affecting the knowledge, teaching, and practice of Ayurveda. It examines the impact and contribution of processes like standardisation, professionalisation, bio-medicalisation and pharmaceuticalisation on Ayurveda education, knowledge, practice and policies. To maintain health and wellbeing Ayurveda's ancient knowledge and practice needs to be applied at individual, community and health care provider levels and not be limited to the medical system. The current over medicalisation of society is a potential threat to human health and well-being. Ayurveda and LHT knowledge can provide essential teachings and practices to counter-balance this current trend through encouraging a population's self-reliance in its health

Application of artificial intelligence to predict flow assisted corrosion in nuclear/thermal power plant

Flow assisted corrosion (FAC) is a wall-thinning phenomena of carbon steel pipe in nuclear and thermal power plant. Due to FAC, many accidents have taken place in nuclear plants resulting in casualties. In FAC, dissolution of iron from the iron-oxide fluid interface at pipe wall takes place and it is affected by pH, oxygen concentration, flow rate, temperature and chromium content of piping material. Due to complex interaction of these parameters, FAC prediction is difficult using conventional modeling tools and experimental evaluation is time consuming and costly. In this work, artificial neural network (ANN) has been used for FAC prediction using 320 data points collected from published literature. The neural network training was carried out using Lavender-Marquardt back-propagation algorithm in Matlab. The results show that ANN is a powerful tool for predicting FAC rate with regression coefficient above 90% and hence it can be very useful by regular training of the model with actual operational data in safety management and long term planning in nuclear/thermal power plant. A sensitivity analysis with respect to each parameter has been carried out using ANN model. It is observed that FAC rate is lower under alkaline conditions and goes through a maxima in a temperature range of 140 to 150°C

Application of artificial intelligence to predict flow assisted corrosion in nuclear/thermal power plant

418-423Flow assisted corrosion (FAC) is a wall-thinning phenomena of carbon steel pipe in nuclear and thermal power plant. Due to FAC, many accidents have taken place in nuclear plants resulting in casualties. In FAC, dissolution of iron from the iron-oxide fluid interface at pipe wall takes place and it is affected by pH, oxygen concentration, flow rate, temperature and chromium content of piping material. Due to complex interaction of these parameters, FAC prediction is difficult using conventional modeling tools and experimental evaluation is time consuming and costly. In this work, artificial neural network (ANN) has been used for FAC prediction using 320 data points collected from published literature. The neural network training was carried out using Lavender-Marquardt back-propagation algorithm in Matlab. The results show that ANN is a powerful tool for predicting FAC rate with regression coefficient above 90% and hence it can be very useful by regular training of the model with actual operational data in safety management and long term planning in nuclear/thermal power plant. A sensitivity analysis with respect to each parameter has been carried out using ANN model. It is observed that FAC rate is lower under alkaline conditions and goes through a maxima in a temperature range of 140 to 150°C