According to the risk investigations of being involved in an accident, alcohol-impaired driving is one of the major causes of motor-vehicles accidents. Preventing highly intoxicated persons from driving would potentially save many lives. This paper proposes a lightweight in-vehicle alcohol detection that processes the data generated from 6-alcohol sensors (MQ-3 Alcohol Sensors) using an optimizable shallow neural network (O-SNN). The experimental evaluation results exhibit a high-performance detection system scoring a 99.8% of detection accuracy with a very short inferencing delay of 2.22 µ seconds. Hence, the proposed model can be efficiently deployed and used to discover in-vehicle alcohol with high accuracy and low inference overhead as a part of the driver alcohol detection system for safety (DADSS) system aiming at massive deployment of alcohol sensing systems that could potentially save thousands of lives annually.

Microgrids are becoming popular nowadays because they provide clean, efficient, and low-cost energy. To use the stored energy in times of emergency or peak loads, microgrids require bulk storage capacity. Since microgrids are the future of renewable energy, the energy storage technology employed should be optimized to generate electricity. Batteries play a variety of essential roles in daily life and are used at peak hours and during a time of emergency. There are different types of batteries i.e., lion batteries, lead-acid batteries, etc. Optimal battery sizing of microgrids is a challenging problem, that limits modern technologies such as electric vehicles, etc. It is important to know different battery features such as battery life, battery throughput, and battery autonomy to get optimal battery sizing for microgrids. Mixed-integer linear programming (MILP) is an established technique for the integration and optimization of different energy sources and parameters for optimal battery sizing. A new MILP based dataset is introduced in this work. Support vector machine (SVM) is the machine learning application used to estimate the optimum battery size. The impact of feature selection algorithms on the proposed machine learning-based model is evaluated. The performance of the six best-performing feature selection algorithms is analyzed. The experimental results show that the feature selection algorithms improve the performance of the proposed methodology. Ranker search shows the best performance with a Spearman’s rank-ordered correlation constant of 0.9756, linear correlation constant of 0.9452, Kendall correlation constant of 0.8488 and root mean squared error of 0.0525.

Since late December 2021, the Hunga Tonga-Hunga Ha'apai underwater volcano has been erupting in a series of large explosions. On Friday, January 15th, 2022, a particularly significant explosion sent a massive ash cloud high into the atmosphere. This study depicts the modeling of the volcanic ash dispersion from the event while incorporating meteorological data assimilation within the inline Weather Research and Forecasting model coupled with chemistry (WRF-Chem). Two forecast scenarios: one with only meteorology and no chemistry (OMET) and one with gas, aerosol chemistry, and no assimilation (NODA) were undertaken and compared to the third forecast impacting data assimilation to distinguish the Flux Adjusting Surface Data Assimilation System (FASDAS)'s effects. Data assimilation, a process that combines real-time observations with numerical models, plays a pivotal role in enhancing the accuracy of various scientific simulations and predictions.Analysis of different scenarios reveals that the FASDAS result in lessening of planetary boundary layer height (PBLH), downward surface shortwave flux, and 2m temperature by up to 800m, 250 W.m-2 and 6°C on the land portion, respectively, while the opposite is observed nearby the eruption site. Model validation against the observations shows that the inclusion of FASDAS nudging in WRF-Chem significantly enhances the model performance in retrieving meteorological variables. The simulations also revealed significant biases in the concentration of volcanic ash around the ash cloud. Additionally, data analysis from the Copernicus TROPOspheric Monitoring Instrument Sentinel-5 Precursor (TROPOMI-S5P) indicates that total SO2 emissions traveled west. This work demonstrates the significant contribution of data assimilation on the results of operational air quality predictions during violent volcanic eruption events.