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In this study, we propose a method to estimate energy consumption in battery-powered Narrowband Internet of Things (NB-IoT) devices using the statistical data available from the NB-IoT modem, thereby circumventing the need for additional circuitry to measure battery voltage or current consumption. A custom edge node with an NB-IoT module and onboard current measurement circuit was developed and utilized to generate a labeled dataset. Each data point, generated upon UDP packet transmission, includes metadata such as radio channel quality parameters, temporal parameters (TX and RX time), transmission and reception power, and coverage extension mode. Feature selection through variance and correlation analysis revealed that coverage extension mode and temporal parameters significantly correlate to the energy consumption. Using these features, we tested 11 machine learning models for energy consumption estimation, assessing their performance and memory footprint, both of which are critical factors for resource-constrained embedded systems. Our best models achieved up to 93.8% of fit with measured values, with memory footprints below 100 KB, some as low as 3 KB. This approach offers a practical solution for the energy consumption estimation in NB-IoT devices without hardware modifications, thereby enabling energy-aware device management.

The purpose of this study was to analyze the association between the number of cigarettes smoked and the length of smoking with mortality among patients who were hospitalized in the intensive care unit (ICU) due to SARS-CoV-2 infection. This retrospective cohort study was conducted at the General Hospital in Sombor (Serbia). Patients who were hospitalized because of severe SARS-CoV-2 infection between March 2021 and March 2023 were included in this study. Data were retrieved from electronic medical records, including those on smoking status, duration of smoking, and the number of cigarettes smoked per day. Of 307 patients whose medical records were analyzed, 40.7% were current smokers. Current smokers more often required treatment in the ICU, where they also had a higher mortality rate compared to current non-smokers. Longer duration of smoking was independently associated with dying of SARS-CoV-2 infection in the ICU. The Kaplan-Meier survival curve showed that hospitalized patients with SARS-CoV-2 infection who smoked had poorer survival compared to current non-smokers. According to the receiver operating characteristic curve, patients who smoked for more than 40 years had a 73.9% chance of dying from SARS-CoV-2 infection. Current smokers who smoked 22.5 cigarettes per day had a 75.4% chance of dying from SARS-CoV-2 infection in the ICU. Smokers with severe SARS-CoV-2 infection had a higher likelihood of having poor outcomes. Longer duration of smoking was an independent predictor of SARS-CoV-2 mortality. Smoking prevention and smoking cessation are of paramount importance in the prevention of SARS-CoV-2-related mortality.