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Fuel cell technology is increasingly making its forays into the maritime domain. There is a worldwide demand for increased shipboard power and curtailed overall emissions which are particularly evident in the case of warships. Therefore, many attempts have been made across the globe to integrate various kinds of fuel cells into naval propulsion systems for both surface and submersible vessels with varying degrees of successes. Fuel cells are a promising solution offering great operational flexibility along with environmental, economic and military benefits and advantages. However, a majority of the studies or attempts to introduce fuel cells on (war)ships have focused on sating ship service power requirements rather than developing a full power system with a partial, but notable, exception of the fuel cells-based Air Independent Propulsion systems employed by a handful of submarine types around the world. This paper aims to present a review of the technologies and studies focusing on the development of fuel cells for naval applications. Since warships place stringent and more demanding requirements on their equipment, they allow for a more rigorous examination of developing technologies and are hence preferably considered for this study.

Machine learning algorithms can predict stock market fluctuations, control complex manufacturing processes, enable navigation for robots and driverless vehicles, and much more. Now, researchers are tapping a new set of capabilities in this field of artificial intelligence with their new technique. The developed and tested new method allows chemical discoveries to take place quickly and with far less environmental waste than standard large-scale reactions. This paper aims to present a review of the applications of machine learning and artificial intelligence in the field of chemistry. The principal focus of the paper is on organic reaction engineering and its applications such as drug synthesis.

This paper focuses on changing the system of having ‘text’ as password as people tend to often forget their passwords and have to recover it. Proposing the system of having picture as a password which is kept encrypted in a database and decrypted and matched to check for authorization of user. It has been scientifically proven that it’s easier for a human mind to remember a picture, something they can see rather than text.

The Launder–Sharma model (Lett. Heat Mass Transfer 1, 1974, 131–138) is one of the earliest and most-widely used low-Reynolds number (LRN) models and has been shown to be in good agreement with experimental and DNS data for a range of turbulent flow problems, performing better than many other LRN k–e models. However, some recent studies including those using commercial CFD solvers report the model to behave otherwise. In the present study, the LS model has been implemented in FLUENT using user-defined functions (UDFs), and its performance in predicting steady and unsteady turbulent flows has been tested and found to agree closely with those reported in the literature using ‘in-house’ CFD codes. However, this FLUENT-UDF LS model performs very differently from the in-built FLUENT LS model. The former agrees well with experimental and DNS data, whereas the latter does not. In addition, the UDF is used to demonstrate that the model predictions are very sensitive to the interpretation of the model formulation. Consequently, it is concluded that whereas the LS model is not sensitive to numerical method or method of coding, it is sensitive to changes in the interpretation of the formulation of the model.

This project plays a very important role to complement the industrial and automation field. Nowadays, robots are used in several fields of engineering and manufacturing and the systems for controlling or actuating them have also enhanced from the past. The use of gestures for controlling them has been the new trend to control the movement of robotic manipulators. The various methodologies for controlling them are motion tracking, image processing and by using Kinect sensors. All these methods can be used as a teach pendant where one can provide the movement of the manipulator as a preset and the manipulator can carry out the same motion repetitively, or in the case of motion tracking and while using Kinect sensors, the user is bound to a confined area where the cameras can monitor the user’s body. Here, we propose a wireless controlled robotic arm system for tool handling (pick and place) and many other applications where human reach is elusive. The result is that the gestures of the human hand are in sync with the manipulator’s movement. Further, this robotic arm has been implanted beneath a drone which would then have the ability to reach certain heights where human reach is impervious or might put a human’s life in jeopardy. In this case, the user can maneuver along with manipulator wherever it is used.