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Precision agriculture is a breakthrough in digital farming technology, which facilitates the application of precise and exact amount of input level of water and fertilizer to the crop at the required time for increasing the yield. Since agriculture relies on direct rainfall than irrigation and the prediction of rainfall date is easily available from web source, the integration of rainfall prediction with precision agriculture helps to regulate the water consumption in farms. In this work, an edge computing model is developed for predicting soil moisture in real time and managing the water usage in accordance with rain prediction. A soil moisture prediction hybrid algorithm (SMPHA) has been developed that revolves around the decision-making techniques with live environmental parameters including weather parameters for the prediction of soil moisture through the impact of precipitation. Numerous algorithms with the combination of regression + clustering are estimated, and it is inferred that XGBoost + k-means outperforms other algorithmic combinations that is deployed in edge model. This model is used as an intermediary between the end IoT devices and cloud that results in the saving of computationally intensive processing performed on cloud servers. The servers located on a local edge network perform the developed algorithmic computations. Avoiding transmission over the cloud results in significant latency, response time, and computation power savings and therefore increases the efficiency of data transfer. The proposed edge computing model is implemented in Raspberry Pi as an edge, Heroku as cloud, and edge nodes as the combination of Pi with actuators and sensors. The monitored data from Pi are stored in MongoDB webserver that is controlled by Web dashboard. Finally, the developed model is implemented in cloud and edge where the edge server implementation performs better in terms of latency, bandwidth, throughput, response time, and CPU memory usage.

In this paper, we report a facile method to synthesize Ni0.93Co0.02Mn0.05Fe1.95O4−δ (NCMF)@BaTiO3 (BT) core–shell nanoparticles. Flexible thick films of polyvinylidene fluoride (PVDF)-NCMF@BT core–shell nanocomposites with a thickness ∼110 μm were developed. We analysed the impact of the BT shell phase on the magnetic domain distribution and its interaction on the final properties of the composites. The magnetic force microscopy (MFM) microstructure revealed well confined, highly dense, and isolated nanodomains of NCMF. The adequate isolation provided by the BT encapsulation highly reduced the potential of charge leakage among the neighbouring NCMF nanoparticles, and hence, the dielectric loss. Their magnetodielectric measurements demonstrated good sensing performance even at a modest magnetic field of 100 Oe. At 1 kHz, significant magnetocapacitance (MC) and magnetoresistance (MR) values of 1.7% and −4.2% were achieved at 100 Oe and these values increased to a maximum of 4.9% and −10.8% at 1000 Oe for PVDF-50 vol% of the NCMF@BT nanopowder composite, respectively, making them a promising candidate for advanced magnetically ordered pseudocapacitive materials in energy storage in supercapacitor applications. [ABSTRACT FROM AUTHOR]

The city bus drivers are more prone to various physical and mental stress due to the heavy traffic in the city. They are also on shift work causing decrease in the sleep quality which results in increase in stress level and negatively impacts on the health and quality of life. Objective: To assess the stress level among bus drivers in and around city of Chennai Methods: A Cross-sectional study was conducted on a group of 100 city bus drivers from January 2019 to March 2019. The "Perceived Stress Scale (PSS)" questionnaire was used to measure stress level. The Pittsburgh Sleep Quality Index (PSQI) was used to measure of sleep quality. Results: The PSQI scores were poor in 89% of the studied population. The mean PSQI scores were 10.05 and standard deviation of 3.016. Scores of the Perceived Stress Scale shows that 23 % had low stress, 28 % had moderate stress and 49 % had high stress levels. The study shows significant association between stress levels and poor sleep quality. Conclusion: The result of the study show that the bus drivers in the city are stressed and the stress is high during middle age.