Frictional and load-carrying behaviours of micro-textured sector shape pad thrust bearing have been explored and reported herein. The textured pad surfaces have been generated employing different crosssectional shapes (circular, square, trapezoidal and triangular) of grooves. Based on the thermohydrodynamic lubrication analysis, it is observed that when the adopted texture pattern is placed on the pad towards the entry region, it produces substantial reduction in friction coefficient. The texture involving the square cross-sectional shape of grooves has yielded substantial reductions in the friction coefficient in comparison with the conventional plain pad.

Purpose The purpose of this paper is to conceive a new surface texture incorporating a tiny shape among the micro-pockets (with circular, rectangular, trapezoidal and triangular cross-sections) and dimples (cylindrical, hemispherical and ellipsoidal) for exploring to enhance the maximum possible performance behaviors of sector shape pad thrust bearing. Design/methodology/approach Numerical simulation of hydrodynamically lubricated sector shape textured pad thrust bearing has been presented incorporating thermal and cavitation effects. The coupled solution of governing equations (Reynolds equation, film thickness expression, viscosity–temperature relation, energy equation and Laplace equation) has been achieved using finite difference method and Gauss–Seidel iterative scheme. Findings With new textured pads, higher load-carrying capacity and lower coefficient of friction are obtained in comparison to plain sector shape pad. Texture pattern comprising square cross-sectional pockets yields higher load-carrying capacity and lower coefficient of friction in comparison to other cross-sectional shapes (circular, trapezoidal and triangular) of pockets considered herein. Originality/value This study reports a new texture, which involves micro-pockets of square cross-sectional shapes to improve the performance behavior of sector shape pad thrust bearing. About 75 per cent increase in load carrying capacity and 42 per cent reduction in coefficient of friction have been achieved with pad having new texture in comparison to conventional pad.

Over the last few years there has been tremendous growth in the field of healthcare monitoring systems in hospitals and outside of it. Developing wireless health care monitoring devices employing various technologies has become a keen area of interest in India and as well as in other Nations. This proposed work aims to integrate artificial neural intelligence in domain of healthcare monitoring. Wireless body sensor devices have the ability to reach an advance level of human body monitoring utilizing various transmission and data analytics techniques. Implementation of Artificial Neural Fuzzy Inference Systems (ANFIS) would enable the system to work as a smart healthcare system that decides the priority by itself based on the collected psychological parameters from the sensor nodes. Proposed model describes an e-healthcare monitoring system developed for realizing integration of ANFIS in healthcare monitoring systems. The model consists of sensors to collect vital data from patient's body which is then transmitted by Wi-Fi to a central HUB where fuzzy logic converts the raw data in linguistic variable which is trained in ANFIS to get the status of patient. The developed system provides the reliable, accurate and real-time accessible data of patients continuously and transmits the vital information using a dedicated communication module in case of emergency. KeywordsPatient's vital signal monitoring; artificial neural fuzzy inference system (ANFIS); wireless transmission; GSM module.

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This paper proposes a hardware model that provides new fire detection and control mechanism with the interface of artificial neural network and fuzzy logic. This work is based on the integration of hardware module and implementation of artificial neural fuzzy inference system (ANFIS). The hardware consists of temperature sensor, smoke sensor, flame detector and a microcontroller unit. The sensors sense the environment and send data to microcontroller for further processing. Here the microcontroller will work as a control unit. The hardware model of the system also consists of the GSM module for sending the warning message if severe fire exists, and a GPS module in order to indicate the fire location. This technique expresses the idea of implementing Fuzzy logic on the real time data which is collected by the sensors. The system aims to predict fire danger by sensing various parameters i.e. smoke, temperature etc. at the early stage. Artificial neural fuzzy inference system (ANFIS) has been utilized in order to enhance the reliability and certainty of real time fire detection mechanism and to reduce the false alarm rates. The system will focus on collection of data from sensors, data fusion through fuzzy logic and quantification of fire warning level. This neural network based fire alarm system can fuse a variety of data set obtained from sensors and also provide the improved ability to adapt in the environment and predict fire in an accurate manner, which has great significance for the safety of human lives as well as property.