Leveraging Machine Learning based Ensemble Time Series Prediction Model for Rainfall Using SVM, KNN and Advanced ARIMA+ E-GARCH

Today's precipitation is growing increasingly variable, making forecasting increasingly difficult. The Indian Meteorological Department (IMD) currently employs Composite and Stochastic approaches to forecast spring storm precipitation in Asia. As a corollary, planners are unlikely to predict the macroeconomic effects of disasters (due to excessive precipitation) or famine (less precipitation). The amount of precipitation that drops dependent on a variety of factors, including the temperature of the atmosphere, humidity, velocity, mobility, and weather conditions. This paper would then employ the Hybrid time-series predictive ARIMA+ E-GARCH (Exponential Generalized Auto-Regressive Conditional Heteroskedasticity) to predict precise runoff by taking into account different climatic considerations such as maritime tension, water content, relative dampness, min-max heat, heavy ice, geostrophic tallness, breeze patterns, soil dampness, and barometric force. In perspective of RMSE, MAE, and MSE, the proposed hybrid ARIMA+E-GARCH paradigm outperformed single simulations and latest hybrid techniques

Investigating Optimum SiO2 Nanolubrication During Turning of AISI 420 SS

AISI 420 martensitic stainless steel is used for making gas and steam turbine blades, steel balls and medical instruments, due to its anti-corrosive properties. Turning of AISI 420 SS would be a worthy procedure specifically in manufacturing high surface finish parts. In this work, effort has been made to investigate the cooling and lubricating performance of SiO2 (silicon dioxide) nanoparticles at different weight concentrations of 0.1g, 0.5g and 1g mixed in a novel developed base fluid (synthetic). The performance of optimum SiO2 based cutting fluid is evaluated based on the turning process with output responses like surface finish and cutting temperature. Taguchi technique was used with standard L9(3**4) orthogonal array. The responses, surface roughness, and cutting temperature were analyzed using S/N (signal-to-noise) and ANOVA (analysis of variance). This analysis identifies the significant input parameter combination to obtain minimum surface roughness and temperature

Blockchain-Based Cloud-Enabled Security Monitoring Using Internet of Things in Smart Agriculture

The Internet of Things (IoT) has rapidly progressed in recent years and immensely influenced many industries in how they operate. Consequently, IoT technology has improved productivity in many sectors, and smart farming has also hugely benefited from the IoT. Smart farming enables precision agriculture, high crop yield, and the efficient utilization of natural resources to sustain for a longer time. Smart farming includes sensing capabilities, communication technologies to transmit the collected data from the sensors, and data analytics to extract meaningful information from the collected data. These modules will enable farmers to make intelligent decisions and gain profits. However, incorporating new technologies includes inheriting security and privacy consequences if they are not implemented in a secure manner, and smart farming is not an exception. Therefore, security monitoring is an essential component to be implemented for smart farming. In this paper, we propose a cloud-enabled smart-farm security monitoring framework to monitor device status and sensor anomalies effectively and mitigate security attacks using behavioral patterns. Additionally, a blockchain-based smart-contract application was implemented to securely store security-anomaly information and proactively mitigate similar attacks targeting other farms in the community. We implemented the security-monitoring-framework prototype for smart farms using Arduino Sensor Kit, ESP32, AWS cloud, and the smart contract on the Ethereum Rinkeby Test Network and evaluated network latency to monitor and respond to security events. The performance evaluation of the proposed framework showed that our solution could detect security anomalies within real-time processing time and update the other farm nodes to be aware of the situation

Optimal Femto Placement in Enterprise Building

Deployment of Femtocell Base Stations a.k.a Femtos for improving network coverage and data rates in indoors is an emerging practice in cellular systems. However, if Femtos are not placed optimally they do not provide these improvements, thus defeating the purpose of their deployment in indoors in the first place. In this paper, we propose a novel heuristic solution for Femto placement which guarantees minimum Signal to Interference plus Noise Ratio (SINR) using fewer number of Femtos. We define a mathematical model taking into account the installation requirements and threshold SINR for each Femto. Consequently, resulting LPP model is solved to obtain the optimum number of Femtos and their placement locations. Our proposed placement scheme offers 10% improvement in SINR when compare to random placement of Femto

Blockchain-Based Cloud-Enabled Security Monitoring Using Internet of Things in Smart Agriculture

The Internet of Things (IoT) has rapidly progressed in recent years and immensely influenced many industries in how they operate. Consequently, IoT technology has improved productivity in many sectors, and smart farming has also hugely benefited from the IoT. Smart farming enables precision agriculture, high crop yield, and the efficient utilization of natural resources to sustain for a longer time. Smart farming includes sensing capabilities, communication technologies to transmit the collected data from the sensors, and data analytics to extract meaningful information from the collected data. These modules will enable farmers to make intelligent decisions and gain profits. However, incorporating new technologies includes inheriting security and privacy consequences if they are not implemented in a secure manner, and smart farming is not an exception. Therefore, security monitoring is an essential component to be implemented for smart farming. In this paper, we propose a cloud-enabled smart-farm security monitoring framework to monitor device status and sensor anomalies effectively and mitigate security attacks using behavioral patterns. Additionally, a blockchain-based smart-contract application was implemented to securely store security-anomaly information and proactively mitigate similar attacks targeting other farms in the community. We implemented the security-monitoring-framework prototype for smart farms using Arduino Sensor Kit, ESP32, AWS cloud, and the smart contract on the Ethereum Rinkeby Test Network and evaluated network latency to monitor and respond to security events. The performance evaluation of the proposed framework showed that our solution could detect security anomalies within real-time processing time and update the other farm nodes to be aware of the situation

Production of L (+) lactic acid by Lactobacillus delbrueckii immobilized in functionalized alginate matrices

The role of functionalized alginate gels as immobilized matrices in production of L (+) lactic acid by Lactobacillus delbrueckii was studied. L. delbrueckii cells immobilized in functionalized alginate beads showed enhanced bead stability and selectivity towards production of optically pure L (+) lactic acid in higher yields (1.74Yp/s) compared to natural alginate. Palmitoylated alginate beads revealed 99% enantiomeric selectivity (ee) in production of L (+) lactic acid. Metabolite analysis during fermentation indicated low by-product (acetic acid, propionic acid and ethanol) formation on repeated batch fermentation with functionalized immobilized microbial cells. The scanning electron microscopic studies showed dense entrapped microbial cell biomass in modified immobilized beads compared to native alginate. Thus the methodology has great importance in large-scale production of optically pure lactic acid

Nanostructural changes in crystallizable controlling units determine the temperature-memory of polymers

Temperature-memory polymers remember the temperature, where they were deformed recently, enabled by broad thermal transitions. In this study, we explored a series of crosslinked poly[ethylene-co-(vinyl acetate)] networks (cPEVAs) comprising crystallizable polyethylene (PE) controlling units exhibiting a pronounced temperature-memory effect (TME) between 16 and 99 °C related to a broad melting transition (∼100 °C). The nanostructural changes in such cPEVAs during programming and activation of the TME were analyzed via in situ X-ray scattering and specific annealing experiments. Different contributions to the mechanism of memorizing high or low deformation temperatures (Tdeform) were observed in cPEVA, which can be associated to the average PE crystal sizes. At high deformation temperatures (>50 °C), newly formed PE crystals, which are established during cooling when fixing the temporary shape, dominated the TME mechanism. In contrast, at low Tdeform (<50 °C), corresponding to a cold drawing scenario, the deformation led preferably to a disruption of existing large crystals into smaller ones, which then fix the temporary shape upon cooling. The observed mechanism of memorizing a deformation temperature might enable the prediction of the TME behavior and the knowledge based design of other TMPs with crystallizable controlling units