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A conceptual boundary level experiment of heat and mass transfer flow in an MHD viscous nanofluid containing gyrotactic microorganisms around a stretching cylinder has been evaluated in this work. The governing equations of the flow have been executed by the boundary layer phenomena. The primary non-linear PDEs have been turned into a dimensionless form via application of suitable dimensionless variables. The resulting non-similar dimensionless differential equations have been resolved by the EFDM with the help of FORTRAN software version 6.6a. Stability and convergence criteria analysis enabled obtaining the convergent solutions. The consequences of numerous parameters such as the Magnetic parameter, Brownian motion parameter, Thermophoresis parameter, Lewis number, Micro-organism Lewis number, and Bio-convection parameter on the flow have been investigated, and the outcomes have been demonstrated graphically. Finally, the current outcomes were compared with the previously published results. [ABSTRACT FROM AUTHOR]

Climate change has already proven its terrible effect on agriculture. Although greenhouse is already an established system for crop production, with technological advancement it is possible to apply automation in many parts of this greenhouse. Therefore, an automated smart greenhouse based on an adaptive neuro fuzzy inference system (ANFIS) and Internet of Things (IoT) could be the best solution to boost the crop production inside the house. Where, four kind of weather data such as temperature, humidity, sunlight and soil-moisture are being collected by using sensors in real time. These collected data are then feed as input variables to the fuzzy control system. The fuzzy control system manipulate the data and ANFIS then make prediction for optimum values of the weather parameters. Thus farmers can monitor all the data and can decide the best value for temperature and humidity. The end users (farmers) can visualize all the data by a simple mobile app installed on their cell phone. GSM or TCP/IP is being used for all kind of data transferring. The FIS node also utilizes same networks to transfer IoT perception layer data to application layer. To ensure the data security, four types of potential IoT perceptron layer attacks are considered and shown their probability to occur through the confusion matrix. Later, necessary steps are taken to prohibit the attacks. Here winter crops are considered in the final simulation, when the optimum temperature in winter is 24º Celsius and humidity is 76.00%. The system is 93.62% capable to detect any attack or security breach at perception layer with a Precision value of 0.83, recall of 0.78 and FI score is 0.81. In comparison to other recently proposed and available systems, this work also combines IoT technology for identifying data threat on a network transfer with fuzzy set. This approach improves learning efficiency, improves prediction accuracy, and proved to be a feasible and effective automated greenhouse maintenance system. Simultaneously, the data collecting module and presentation schema of data from various sensors, as well as the security subsystem module, achieve cloud data storage and format conversion that is compliant with protocol format data. As a result, it may provide data traceability and durability for customized indoor agriculture quality and safety. Thus this modern greenhouse maintenance system is efficient, cost effective, secure and easy to use. [ABSTRACT FROM AUTHOR]