Solar Flare Prediction and Feature Selection using Light Gradient Boosting Machine Algorithm

Solar flares are among the most severe space weather phenomena, and they have the capacity to generate radiation storms and radio disruptions on Earth. The accurate prediction of solar flare events remains a significant challenge, requiring continuous monitoring and identification of specific features that can aid in forecasting this phenomenon, particularly for different classes of solar flares. In this study, we aim to forecast C and M class solar flares utilising a machine-learning algorithm, namely the Light Gradient Boosting Machine. We have utilised a dataset spanning 9 years, obtained from the Space-weather Helioseismic and Magnetic Imager Active Region Patches (SHARP), with a temporal resolution of 1 hour. A total of 37 flare features were considered in our analysis, comprising of 25 active region parameters and 12 flare history features. To address the issue of class imbalance in solar flare data, we employed the Synthetic Minority Oversampling Technique (SMOTE). We used two labeling approaches in our study: a fixed 24-hour window label and a varying window that considers the changing nature of solar activity. Then, the developed machine learning algorithm was trained and tested using forecast verification metrics, with an emphasis on evaluating the true skill statistic (TSS). Furthermore, we implemented a feature selection algorithm to determine the most significant features from the pool of 37 features that could distinguish between flaring and non-flaring active regions. We found that utilising a limited set of useful features resulted in improved prediction performance. For the 24-hour prediction window, we achieved a TSS of 0.63 (0.69) and accuracy of 0.90 (0.97) for $\geq$C ($\geq$M) class solar flares.Comment: Accepted for publication in Solar Physics journa

SWASTi-CME: A physics-based model to study CME evolution and its interaction with Solar Wind

Coronal mass ejections (CMEs) are primary drivers of space weather and studying their evolution in the inner heliosphere is vital to prepare for a timely response. Solar wind streams, acting as background, influence their propagation in the heliosphere and associated geomagnetic storm activity. This study introduces SWASTi-CME, a newly developed MHD-based CME model integrated into the Space Weather Adaptive SimulaTion (SWASTi) framework. It incorporates a non-magnetized elliptic cone and a magnetized flux rope CME model. To validate the model's performance with in-situ observation at L1, two Carrington rotations were chosen: one during solar maxima with multiple CMEs, and one during solar minima with a single CME. The study also presents a quantitative analysis of CME-solar wind interaction using this model. To account for ambient solar wind effects, two scenarios of different complexity in solar wind conditions were established. The results indicate that ambient conditions can significantly impact some of the CME properties in the inner heliosphere. We found that the drag force on the CME front exhibits a variable nature, resulting in asymmetric deformation of the CME leading edge. Additionally, the study reveals that the impact on the distribution of CME internal pressure primarily occurs during the initial stage, while the CME density distribution is affected throughout its propagation. Moreover, regardless of the ambient conditions, it was observed that after a certain propagation time (t), the CME volume follows a non-fractal power-law expansion ($\propto t^{3.03-3.33}$) due to the attainment of a balanced state with ambient.Comment: Accepted for publication in ApJ

SWASTi-CME: A Physics-based Model to Study Coronal Mass Ejection Evolution and Its Interaction with Solar Wind

Coronal mass ejections (CMEs) are primary drivers of space weather, and studying their evolution in the inner heliosphere is vital to prepare for a timely response. Solar wind streams, acting as background, influence their propagation in the heliosphere and associated geomagnetic storm activity. This study introduces SWASTi-CME, a newly developed MHD-based CME model integrated into the Space Weather Adaptive SimulaTion (SWASTi) framework. It incorporates a nonmagnetized elliptic cone and a magnetized flux rope CME model. To validate the model’s performance with in situ observation at L1, two Carrington rotations were chosen: one during solar maxima with multiple CMEs, and one during solar minima with a single CME. The study also presents a quantitative analysis of CME–solar wind interaction using this model. To account for ambient solar wind effects, two scenarios of different complexity in solar wind conditions were established. The results indicate that ambient conditions can significantly impact some of the CME properties in the inner heliosphere. We found that the drag force on the CME front exhibits a variable nature, resulting in asymmetric deformation of the CME leading edge. Additionally, the study reveals that the impact on the distribution of CME internal pressure primarily occurs during the initial stage, while the CME density distribution is affected throughout its propagation. Moreover, regardless of the ambient conditions, it was observed that, after a certain propagation time ( t ), the CME volume follows a nonfractal power-law expansion (∝ t ^3.03−3.33 ) due to the attainment of a balanced state with ambient

An Unusual Presentation of Oral Mucocele in Infant and Its Review

Mucocele is a benign lesion characterized by an extravasation or retention of mucous in submucosal tissue from minor salivary glands. Mucoceles are known to occur most commonly on the lower lip, followed by the floor of mouth and buccal mucosa being the next most frequent sites. Trauma and lip biting habits are the main cause for these types of lesions. Mucocele is a common oral mucosal lesion but it is rarely observed in the infant. This paper highlights the successful management of a rare case of mucocele in an 11-month-old child. Diagnosis and management of mucocele are challenging. For this reason we felt it would be interesting to review the clinical characteristics, histological features, differential diagnosis, and their treatment and evolution in order to aid decision-making in daily clinical practice

Proceedings of International Conference on Women Researchers in Electronics and Computing

This proceeding contains articles on the various research ideas of the academic community and practitioners presented at the international conference, “Women Researchers in Electronics and Computing” (WREC’2021). WREC'21 was organized in online mode by Dr. B R Ambedkar National Institute of Technology, Jalandhar (Punjab), INDIA during 22 – 24 April 2021. This conference was conceptualized with an objective to encourage and motivate women engineers and scientists to excel in science and technology and to be the role models for young girls to follow in their footsteps. With a view to inspire women engineers, pioneer and successful women achievers in the domains of VLSI design, wireless sensor networks, communication, image/ signal processing, machine learning, and emerging technologies were identified from across the globe and invited to present their work and address the participants in this women oriented conference. Conference Title: International Conference on Women Researchers in Electronics and ComputingConference Acronym: WREC'21Conference Date: 22–24 April 2021Conference Location: Online (Virtual Mode)Conference Organizers: Department of Electronics and Communication Engineering, Dr. B. R. Ambedkar National Institute of Technology, Jalandhar, Punjab, INDI

Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950–2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021

BackgroundEstimates of demographic metrics are crucial to assess levels and trends of population health outcomes. The profound impact of the COVID-19 pandemic on populations worldwide has underscored the need for timely estimates to understand this unprecedented event within the context of long-term population health trends. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 provides new demographic estimates for 204 countries and territories and 811 additional subnational locations from 1950 to 2021, with a particular emphasis on changes in mortality and life expectancy that occurred during the 2020–21 COVID-19 pandemic period.Methods22 223 data sources from vital registration, sample registration, surveys, censuses, and other sources were used to estimate mortality, with a subset of these sources used exclusively to estimate excess mortality due to the COVID-19 pandemic. 2026 data sources were used for population estimation. Additional sources were used to estimate migration; the effects of the HIV epidemic; and demographic discontinuities due to conflicts, famines, natural disasters, and pandemics, which are used as inputs for estimating mortality and population. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate under-5 mortality rates, which synthesised 30 763 location-years of vital registration and sample registration data, 1365 surveys and censuses, and 80 other sources. ST-GPR was also used to estimate adult mortality (between ages 15 and 59 years) based on information from 31 642 location-years of vital registration and sample registration data, 355 surveys and censuses, and 24 other sources. Estimates of child and adult mortality rates were then used to generate life tables with a relational model life table system. For countries with large HIV epidemics, life tables were adjusted using independent estimates of HIV-specific mortality generated via an epidemiological analysis of HIV prevalence surveys, antenatal clinic serosurveillance, and other data sources. Excess mortality due to the COVID-19 pandemic in 2020 and 2021 was determined by subtracting observed all-cause mortality (adjusted for late registration and mortality anomalies) from the mortality expected in the absence of the pandemic. Expected mortality was calculated based on historical trends using an ensemble of models. In location-years where all-cause mortality data were unavailable, we estimated excess mortality rates using a regression model with covariates pertaining to the pandemic. Population size was computed using a Bayesian hierarchical cohort component model. Life expectancy was calculated using age-specific mortality rates and standard demographic methods. Uncertainty intervals (UIs) were calculated for every metric using the 25th and 975th ordered values from a 1000-draw posterior distribution.FindingsGlobal all-cause mortality followed two distinct patterns over the study period: age-standardised mortality rates declined between 1950 and 2019 (a 62·8% [95% UI 60·5–65·1] decline), and increased during the COVID-19 pandemic period (2020–21; 5·1% [0·9–9·6] increase). In contrast with the overall reverse in mortality trends during the pandemic period, child mortality continued to decline, with 4·66 million (3·98–5·50) global deaths in children younger than 5 years in 2021 compared with 5·21 million (4·50–6·01) in 2019. An estimated 131 million (126–137) people died globally from all causes in 2020 and 2021 combined, of which 15·9 million (14·7–17·2) were due to the COVID-19 pandemic (measured by excess mortality, which includes deaths directly due to SARS-CoV-2 infection and those indirectly due to other social, economic, or behavioural changes associated with the pandemic). Excess mortality rates exceeded 150 deaths per 100 000 population during at least one year of the pandemic in 80 countries and territories, whereas 20 nations had a negative excess mortality rate in 2020 or 2021, indicating that all-cause mortality in these countries was lower during the pandemic than expected based on historical trends. Between 1950 and 2021, global life expectancy at birth increased by 22·7 years (20·8–24·8), from 49·0 years (46·7–51·3) to 71·7 years (70·9–72·5). Global life expectancy at birth declined by 1·6 years (1·0–2·2) between 2019 and 2021, reversing historical trends. An increase in life expectancy was only observed in 32 (15·7%) of 204 countries and territories between 2019 and 2021. The global population reached 7·89 billion (7·67–8·13) people in 2021, by which time 56 of 204 countries and territories had peaked and subsequently populations have declined. The largest proportion of population growth between 2020 and 2021 was in sub-Saharan Africa (39·5% [28·4–52·7]) and south Asia (26·3% [9·0–44·7]). From 2000 to 2021, the ratio of the population aged 65 years and older to the population aged younger than 15 years increased in 188 (92·2%) of 204 nations.InterpretationGlobal adult mortality rates markedly increased during the COVID-19 pandemic in 2020 and 2021, reversing past decreasing trends, while child mortality rates continued to decline, albeit more slowly than in earlier years. Although COVID-19 had a substantial impact on many demographic indicators during the first 2 years of the pandemic, overall global health progress over the 72 years evaluated has been profound, with considerable improvements in mortality and life expectancy. Additionally, we observed a deceleration of global population growth since 2017, despite steady or increasing growth in lower-income countries, combined with a continued global shift of population age structures towards older ages. These demographic changes will likely present future challenges to health systems, economies, and societies. The comprehensive demographic estimates reported here will enable researchers, policy makers, health practitioners, and other key stakeholders to better understand and address the profound changes that have occurred in the global health landscape following the first 2 years of the COVID-19 pandemic, and longer-term trends beyond the pandemic